Copyright

by

Heath Marcus Burton

2019

The Dissertation Committee for Heath Marcus Burton Certifies that this is the

approved version of the following dissertation:

Effects of Inactivity on Cardio-Metabolic Responses to

Exercise

Committee:

Edward F. Coyle, Supervisor

Audrey J. Stone

Harold W. Kohl, III

Molly S. Bray

Effects of Inactivity on Cardio-Metabolic Responses to

Exercise

by

Heath Marcus Burton

Dissertation

Presented to the Faculty of the Graduate School of

The University of Texas at Austin

in Partial Fulfillment

of the Requirements

for the Degree of

Doctor of Philosophy

The University of Texas at Austin

December 2019

Dedication

To my wife, Cassady. For your unending love and support without which none of this

would be possible.

v

Acknowledgements

I would first thank my advisor and mentor, Dr. Edward F. Coyle, for his guidance

and support throughout my years at The University of Texas at Austin, culminating in this

dissertation. I will always be grateful for the experience gained under your leadership. Your

willingness to challenge me with new opportunities and responsibilities has fostered an

environment where I have been able to grow as an exercise scientist and a person. It has

been an honor to work closely and learn from you each day over the past 5 years.

A special thanks to each member of the Human Performance Laboratory during my

time here. To Brian Leary, Anthony Wolfe, Emre Vardarli, Ting Chou, John Akins, Kiki

Crawford, Remzi Satiroglu, Ryan Bjellquist-Ledger, Jakob Allen, Mike Dial, Rebecca

Braden, Dongwoo Hanh, Luke Montzingo, and Mike Brenneman, each one of you has

played an instrumental role, in your own way, in the fulfillment of this work. It has been

an honor and a privilege to work alongside some of the best scientist and people I have

ever had the pleasure of working with.

To my parents, Michael and Stacy, and my brothers, Hunter and Peyton, thank you

for your love and encouragement throughout my graduate studies. Even from sixteen hours

away, your words of affirmation and belief in me had a profound impact on my ability to

reach this day. Thank you to all my friends and family for your unwaivering love, support,

and encouragement: Jimmy and Karen Kendrick, William and Hannah Kendrick, Lindsey

Burton, Toriano and Natalie Mayo, and Keith and Nancy Phillips.

vi

Abstract

Effects of Inactivity on Cardio-Metabolic Responses to

Exercise

Heath Marcus Burton, Ph.D.

The University of Texas at Austin, 2019

Supervisor: Edward F. Coyle

Physical inactivity has been known to cause deleterious health effects. New

evidence suggests current physical activity recommendations may not be enough to reduce

the risk of developing cardiovascular disease and mortality for those experiencing high

levels of physical inactivity (e.g.; prolonged sitting). The purpose of study one was to

determine if daily physical inactivity in a group taking low steps (i.e.; 4,767377 steps/day,

LS) impairs postprandial lipemia (PPL), fat oxidation, and submaximal exercise responses

to short term training, compared to a group taking high steps (16,048725 steps/day; HS).

After an initial high fat tolerance test (HFTT) to establish baseline responses to a high fat

meal, participants (n=16) completed an 11-day training program with assigned step counts

and five exercise training bouts consisting of 20 minutes of cycling at 80% VO2peak and

two 5-minute intervals at 90% VO2peak. The day following the first and final bouts of

exercise training, participants completed a second and third HFTT, respectively, to assess

acute responses of PPL to the training. Within HS, a 31% reduction (p<0.05) was observed

vii

in plasma triglyceride incremental area under the curve (AUCI) after acute, as well as a

27% reduction (p<0.05) following chronic training. Further in HS, but not LS, there were

significant (p<0.05) reductions in markers of stress during submaximal exercise, such as

blood lactate and heart rate, after training. These findings suggest step reductions can lead

to an impaired ability to adapt to short term exercise training. The purpose of study two

was to determine the effect of reducing step count over two days on the ability of a 1-h

bout of exercise to reduce PPL. Participants (n= 10) completed three trials: Low

(2,675314 steps/day), Limited (4,759276 steps/day) and Normal Activity (8,481581

steps/day) for two days followed by a 1-h bout of treadmill running at 64% VO2max with

a HFTT the following morning. PPL responses following 2,675 and 4,759 step/day trials

did not differ. However, following exercise in a condition of 8,481 steps/day, AUCI was

reduced 22% and 23% (p<0.05) compared to the 2,675 and 4,759 step/day trials,

respectively. This suggests that a 1-h bout of running has a decreased ability to lower PPL

the next day when taking 4,759 steps/day or less. Taken together these studies highlight

the importance of maintaining a healthy level of daily non-exercise physical activity,

regardless of participation in exercise. From these studies it is recommended that

individuals maintain a daily step count of at least 8,500 steps in additional to any planned

exercise in order to achieve improvements in PPL as a result of acute or chronic exercise.

viii

Table of Contents

List of Tables ...........................................................................................................x

List of Figures ...................................................................................................... xiii

Chapter I: General Introduction ..............................................................................1

Chapter II: Purpose and Hypothesis .......................................................................4

Chapter III: Study #1 ..............................................................................................6

The Effect of Prolonged Sitting on Cardio-Metabolic Responses to Short Term

Exercise Training ...................................................................................6

Abstract .........................................................................................6

Introduction ...................................................................................8

Methods.......................................................................................10

Results .........................................................................................17

Discussion ...................................................................................21

Tables and Figures ......................................................................28

Chapter IV: Study #2 ............................................................................................37

Dose Response of Physical Inactivity on Plasma Triglycerides After a Meal37

Abstract .......................................................................................37

Introduction .................................................................................39

Methods.......................................................................................42

Results .........................................................................................47

Discussion ...................................................................................50

Tables and Figures ......................................................................56

Chapter VI: General Summary .............................................................................66

Chapter VII: Review of Literature ........................................................................69

Introduction .................................................................................69

Postprandial Metabolism and Health ..........................................71

Prevalence of Inactivity in Modern Culture................................74

Deleterious Effects of Inactivity .................................................77

ix

Exercise and Postprandial Metabolism .......................................79

Alterations in Ambulatory Activity ............................................85

Exercise Resistance .....................................................................88

Possible Mechanisms Inducing Exercise Resistance ..................93

Future Work ................................................................................95

Appendices .............................................................................................................98

Appendix A: Methodological Techniques ....................................................98

Appendix B: Research Consent Forms .......................................................103

Appendix C: Health History Questionnaire ................................................115

Appendix D: Additional Tables for Study 1 ...............................................121

Appendix E: Additional Tables & Figures for Study 2 ..............................126

Appendix F: Bihourly RER measurements.................................................128

Appendix G: Study 1 Individual Data Tables ............................................131

Biographical and VO2peak Data .................................................131

Submaximal Exercise Data .......................................................132

Daily Steps ................................................................................133

Plasma Triglyceride Concentrations .........................................134

Plasma Glucose Concentrations ................................................136

RER Data ..................................................................................138

Postprandial Fat Oxidation .......................................................140

Appendix H: Study 2 Individual Data Tables ............................................141

Biographical & Exercise Data ..................................................141

Daily Steps ................................................................................142

Plasma Triglyceride Concentrations .........................................143

Plasma Glucose Concentrations ................................................144

RER Data ..................................................................................145

Postprandial Fat Oxidation .......................................................146

x

List of Tables

Table 1. Descriptive Statistics of the two groups (i.e. High Step and Low Step) at the

beginning of the study. All Data are reported as Mean SE............34

Table 2. Physiological responses to maximal and submaximal exercise testing. (*)

significantly different from pre-testing within treatment group, p<0.05.

(†) significantly different from pre-testing within treatment group,

p<0.01. All Data are reported as Mean SE. ...................................35

Table 3. Overall postprandial substrate oxidation during HFTTs at Baseline,

following a single bout of exercise (Acute), and following 5 training

bouts over the 9-days of training (Post Training). (*) Significantly

different from Baseline, (p<0.05). Data reported MeanSE. ...........36

Table 4. Descriptive Statistics for participants at the beginning of the study. All Data

are reported as Mean SE. ...............................................................61

Table 5. Responses to maximal exercise and the 1-h bout of submaximal exercise.

All data are reported as Mean SE. .................................................62

Table 6. Average daily steps were measured via activPal activity monitor, attached

on the participant’s anterior thigh throughout each trial. Average daily

step counts for each trial are presented for Control (C1 & C2) and

Intervention Phases (D1 & D2). (*) significantly different from Low,

p<0.05. (**) significantly different from Low, p<0.01. (†) significantly

different from Low & Limited step trial, p<0.05. .............................63

xi

Table 7. Hourly responses (e.g.; H2, H3, etc.) of plasma triglyceride and plasma

glucose concentrations during HFTT for each trial. (*) Significantly

different from Low, p<0.05. (†) Significantly different from Limited,

p<0.05. Data reported MeanSE. .....................................................64

Table 8. Overall postprandial substrate oxidation during HFTT for each trial. (*)

significantly different from Low & Limited, p<0.05. Data reported

MeanSE...........................................................................................65

Table 9. Summary of findings (*) signifies impaired metabolism ........................68

Table 10. Average Daily Steps for both treatment groups during 11-day intervention

(D4- D14). Average daily steps were significantly different between

groups for each day measured (p<0.001).(*) significantly different from

D12 and D14 within treatment group (p<0.05). Data are presented as

MSE. .............................................................................................121

Table 11. Total and incremental areas under the curve of plasma triglyceride

concentrations during HFTTs at Baseline, following a single bout of

exercise (Acute), and following 5 training bouts over the 9-days of

training (Post Training). (*) Significantly different from Baseline,

p<0.05. (†) significantly different from Baseline, p<0.01. All Data are

reported as Mean SE. ...................................................................122

Table 12. Total and incremental areas under the curve of plasma glucose

concentrations during HFTTs at Baseline, following a single bout of

exercise (Acute), and following 5 training bouts over the 9-days of

training (Post Training). All Data are reported as Mean SE........123

xii

Table 13. Temporal Responses of plasma triglyceride concentration for High Step

treatment during HFTT at Baseline, following a single bout of exercise

(Acute), and following 5 training bouts over the 9-days of training (Post

Training). (*) significantly different from Baseline, p< 0.05. (†)

significantly different from Baseline, p< 0.01. Data reported MeanSE.

.........................................................................................................124

Table 14. Temporal Responses of plasma triglyceride concentration for Low Step

treatment during High Fat Tolerance Test at Baseline, following a single

bout of exercise (Acute), and following 5 training bouts over the 9-days

of training (Post Training). Data reported MeanSE. .....................125

Table 15. Total and Incremental areas under the curve of plasma triglyceride &

glucose concentrations during HFTT for each trial. (*) Significantly

different from Low & Limited step group, p<0.05. (†) Significantly

different from Low step group, p<0.01 Data reported MeanSE. ..127

Table 16. Average postprandial substrate oxidation at each measurement for HS

Treatment group. (*) Significantly different from Baseline, (p<0.05). All

Data are reported as Mean SE. ....................................................128

Table 17. Average postprandial substrate oxidation at each measurement for LS

Treatment group. All Data are reported as Mean SE. ..................129

Table 18. Average postprandial substrate oxidation at each measurement for each

trial. (*) significantly different from both Low & Limited, p<0.05. All

Data are reported as Mean SE. ....................................................130

xiii

List of Figures

Figure 1. Study Design. Participants were separated into two groups (High Step or

Low Step) and completed a short-term exercise regime with

physiological and metabolic testing pre and post training. Subjects took

their assigned step number on days 4-14. .........................................28

Figure 2. Daily steps were measured via activPal activity monitor, attached on the

participant’s anterior thigh throughout each trial. Average daily step

count, for each group are presented for the 11-day intervention period

(D4-D14). Average daily steps were significantly different between

groups for every day measured (p<0.001). (*) significantly different

from D12 and D14 within treatment group (p<0.05). .......................29

Figure 3. Total and Incremental areas under the curve of plasma triglyceride

concentration during HFTT at Baseline, following a single bout of

exercise (Acute), and following 5 training bouts over the 9-days of

training (Post Training). (*) Significantly different from Baseline within

treatment group, p<0.05. (†) significantly different from Baseline within

treatment group, p<0.01. Data reported MeanSE. ..........................30

Figure 4. Total and Incremental areas under the curve of plasma glucose

concentration during HFTT at Baseline, following a single bout of

exercise (Acute), and following 5 training bouts over the 9-days of

training (Post Training). Data reported MeanSE. ...........................31

xiv

Figure 5. Temporal Responses of plasma triglyceride & plasma glucose

concentrations for High Step treatment during the HFTT at Baseline,

following a single bout of exercise (Acute), and following 5 training

bouts over the 9-days of training (Post Training). (*) Acute significantly

different from Baseline, p< 0.05. (†) Acute & Post Training significantly

different from Baseline, p< 0.01. (#) Post Training significantly different

from Baseline, p<0.05. Data reported MeanSE. .............................32

Figure 6. Temporal Responses of plasma triglyceride & glucose concentrations for

Low Step treatment during HFTT at Baseline, following a single bout of

exercise (Acute), and following 5 training bouts over the 9-days of

intervention (Post Training). Data reported MeanSE. ....................33

Figure 7. Study Design. Participants completed a five-day randomized, crossover

experimental design with differing levels of daily step reduction (i.e.

Low- 2,675, Limited- 4,759, & Normal Activity-8,480 Steps/Day).

Participants completed two control days with activity monitoring before

the initiation the two-day step reduction (D1 & D2). Participants also

completed an hour of treadmill running on the night of D2 followed by

HFTT on the morning of D3. ............................................................56

Figure 8. Total and Incremental areas under the curve of plasma triglyceride

concentrations during HFTT for each trial. (*) significantly different

from Low & Limited step group, p<0.05. (†) significantly different from

Low step group, p<0.01. Data reported as MeanSE. ......................57

Figure 9. Total and Incremental areas under the curve of plasma glucose

concentrations during HFTT for each trial. Data reported as MeanSE.

...........................................................................................................58

xv

Figure 10. Temporal Responses of plasma triglyceride concentrations for each trial

during HFTT. (*) Normal significantly different from Low, p<0.05. (†)

Normal significantly different from Limited, p<0.05. (#) Normal

significantly different from Low, p<0.01. Data reported as MeanSE.

...........................................................................................................59

Figure 11. Temporal Responses of plasma glucose concentrations for each trial

during HFTT. Data reported as MeanSE. .......................................60

Figure 12. Diagram of OxiplexTS probe for measuring deoxygenated hemoglobin in

skeletal muscle during submaximal exercise ....................................99

Figure 13. Average daily steps were measured via activPal activity monitor, attached

on the participant’s anterior thigh throughout each trial. Average daily

step counts for each trial are presented for Control (C1 & C2) and

Intervention Phases (D1 & D2). (*) significantly different from Low,

p<0.05. (**) significantly different from Low, p<0.01. (†) significantly

different from Low & Limited step trial, p<0.05. ...........................126

1

Chapter I: General Introduction

The consequences of physical inactivity have been recognized for four millennia or

more (19, 20, 112) and the evidence for health benefits of physical activity have been

clearly documented (68, 203, 208). Nevertheless, physical inactivity continues to be a

growing problem for the health and wellness of large swaths of the population worldwide

(105). Physical activity has been almost systematically engineered out of the daily lives of

many, with advancements in automation and mechanical transport, drastically increasing

the amounts sedentary time and inactivity overall (9, 26). The prevalence and effects of

physical inactivity on health result in an impact on the population that, while not given the

same attention, is at least as deleterious as that of smoking and obesity (112). However

drastic the consequence, there seems to be no abatement of this trend in sight (65).

From the work of Morris (133) through present day investigations physical

inactivity has been directly linked to at least 35 chronic diseases directly linked to physical

inactivity (20). Of note in the diseases linked to physical inactivity is atherosclerotic

cardiovascular disease, which remains the leading cause of both death and disability

worldwide (6, 78). In 1979, Zilversmit et al. (223) characterized atherosclerosis as a

postprandial phenomenon. The postprandial or non-fasting state predominates the waking

hours of individuals in most developed countries, whose population rarely go more than 6-

8 hours without eating a meal. A rise in physical inactivity in these countries leads to

scenario in which these two regularly coincide and may further amplify the elevation of

plasma triglycerides which typically peaks 3-5 hours after a meal. If these plasma

2

triglycerides remain abnormally elevated the result is the deposition of fatty plaques in the

arterial walls which characterizes atherosclerosis (223).

Current recommendations advise combatting the development of cardiovascular

diseases, like atherosclerosis, with 30 minutes of moderate-to-vigorous physical activity a

day or 150 minutes per week (149, 157, 208). Indeed, available evidence clearly documents

the ability of a single bout of exercise to attenuate the exaggerated rise is plasma

triglycerides following a meal, or postprandial lipemia (PPL), in individuals who are

physically active and who normolipidimic (52, 77, 83, 120) and hyperlipidemic (124, 221)

and across a range of training status (54, 66, 77). Although these well-established (49) and

newly formed recommendations (39) for exercise are firmly grounded in data driven

conclusions, it seems some individuals who achieve levels of activity commensurate with

these recommendations are not incurring the protective effects of said activity (1, 16, 98,

150, 198, 218). In several recent studies, inactivity has been shown to prevent individuals

from realizing classic improvements in postprandial triglyceride (1, 98) and glucose (38)

metabolism following a bout of exercise. Similar impairments have also been shown to

occur in protein synthesis following prolonged inactivity (22).

This alarming phenomenon, which has been termed ‘exercise resistance’(98), has

just begun to be examined and warrants much more investigation. These studies indicate

that efficacy of exercise, per se, may be diminished or even abolished if it does not coincide

with a healthy lifestyle characterized by daily physical activity. Therefore, it is important

to carefully control levels of daily physical activity when examining the protective effect

of exercise on PPL. While previous investigations have evaluated the response to a single

3

bout of exercise, there are no studies that have investigated the effect of more regular

exercise bouts over a few weeks, in conjunction with a physically inactive lifestyle.

Further, the current literature does not clearly delineate at what levels of physical inactivity

this phenomenon becomes significant. In these studies, we examined 1) the effect of an

accumulated training stimulus (i.e. 5 training bouts) on PPL and other classic responses to

exercise and 2) the PPL response to exercise across three levels physical inactivity.

4

Chapter II: Purpose and Hypothesis

STUDY #1: Study 1 focused on the adaptations to 5 bouts of exercise training at high

intensities in two separate groups with a low and a high level of physical activity. The

purpose of study 1 was to determine if the background level of daily physical inactivity

(e.g.; ~4,767 steps/day) impairs postprandial lipemia (PPL) compared to an active control

condition (e.g.; 16,048 steps/day) and to determine if the background level of daily

physical activity impairs other cardiovascular and metabolic adaptations to short term

training compared to an active control condition. We hypothesized that a low daily step

count treatment would lead to a lower responsiveness to the exercise training compared

with a high step count group. The measurements made include the area under the curve

for plasma triglyceride (PPL) and fat oxidation in response to a high fat meal, as well as

post-intervention measures of peak oxygen consumption (VO2peak) and submaximal

heart rate, blood lactate concentration and muscle deoxygenation.

STUDY #2: Study 2 focused on the effect of inactivity, measured by daily step count for

2 days on the ability of acute 1-h bout of moderate intensity exercise to reduce the

postprandial plasma triglyceride and plasma glucose responses to a meal high in fat and

glucose. The purpose of study 2 was to determine the effect of altering daily step counts

for two days (i.e.; 2,675, 4,759, and 8,481 steps/day) on the ability of a 1-h bout of

moderate-intensity exercise to reduce PPL. We hypothesized that increasing daily step

counts would lead to greater reductions in area under the curve for plasma triglyceride in

5

response to a high fat meal as well increases in fat oxidation, in response to acute

exercise. This pattern might allow recommendations about the number of steps per day

needed to achieve a healthy PPL and fat oxidation responses following a high fat meal.

6

Chapter III: Study #1

THE EFFECT OF PROLONGED SITTING ON CARDIO-METABOLIC RESPONSES TO SHORT

TERM EXERCISE TRAINING

Abstract

Background: The effects of exercise and physical inactivity on development of

cardiovascular disease have been evaluated individually in numerous investigations. Yet

in reality the two interact but the concurrent effects have yet to be fully described.

Therefore, the objective of the current study was to investigate in young adults, whether an

inactive group (<5,000 steps/day) responds similarly to short-term aerobic exercise training

compared to a highly active group (>15,000 steps/day)

Methods: Sixteen initially sedentary participants completed an intense short-term training

protocol while taking 4,767377 steps/day (n=8) or 16,048725 steps/day (n=8).

Participants completed five bouts of training at ~79% VO2peak. Following acute exercise

and short-term training, metabolic responses to a high fat meal (i.e. plasma triglyceride and

glucose excursions and areas under the curve and fat oxidation) were assessed during a 6-

hour high fat tolerance test (HFTT) on the morning after exercise and compared to a non-

exercise baseline HFTT completed before the initiation of the training. Additionally,

submaximal exercise responses were recorded during 15-minute cycling test (~79%

VO2peak), including: heart rate, blood lactate, and deoxygenated hemoglobin were

compared within and between groups, before and after training.

Results: Maintaining 16,048 steps/day while completing short-term exercise training

resulted in reduced incremental area under the curve (AUCI) for plasma triglyceride

concentrations by 31% after acute exercise and by 27% after chronic training, compared to

baseline (p<0.05). This was accompanied by increased whole-body fat oxidation (p<0.05).

Further, muscle stress during submaximal exercise, as marked by heart rate, blood lactate

7

and deoxygenated hemoglobin, was also reduced (p<0.05). Despite completing the same

training regimen, participants taking <5,000 steps/day showed no significant

improvements in postprandial responses or markers of stress during submaximal exercise

after training (p>0.05). However, the two groups showed a similar increase in VO2peak.

Conclusion: In conclusion, when completing a 5-bout exercise training program at

vigorous intensity, decreasing daily steps to approximately 5,000 steps/day appears to

prevent or significantly blunt some of the classic cardio-metabolic adaptations that occur

with 16,000 steps/day. Based on these findings, it appears that the effects of inactivity cause

a blunting of the normal adaptations to exercise including both cardio-metabolic measures

as well as exercise stress measures such as heart rate, blood lactate concentrations, and

deoxygenation of hemoglobin in the active muscle, but not VO2peak.

8

Introduction

The modernization of society has led to vast technological advancements that have

changed life throughout the world. These advances have intended to improve the quality

of life for many individuals, and to a degree, increased automation has done so. However,

the resultant increased physical sedentary time in modernized cultures has been

accompanied with serious problems. Public health professionals assert that individuals in

modern society have become a victim of their own success, in which physical activity have

been systematically “engineered out” of daily life, and further argue that this has

compromised the health of many (100).

Couple this with improvements in agriculture and sustainability and people are

spending more time being inactive in a fed state. Ingestion of fatty foods causes a

concomitant rise in chylomicron triglyceride concentration in the blood as lipids from

digestion begin to enter the blood and are subsequently cleared into other tissues over the

course of up to 8-12 h (32, 56, 86). The rise in chylomicron triglyceride in the blood after

a meal high in fat produces postprandial lipemia (PPL). The high concentration of

triglycerides in the plasma leads to subsequent breakdown into more atherosclerotic

byproducts, resulting in the possible formation of atherosclerotic plaques in arteries (135).

Along with the rise in sedentary behavior has come an increase in the mortality rate as

caused by cardiovascular disease (137). As early as 1979, atherosclerosis was described as

a “postprandial phenomenon” (223). Exaggerated rises plasma triglyceride concentration

9

after a meal (32, 56, 86) caused by a variety of factors including extended periods of

inactivity (3, 33, 51, 135) may be driving the pathogenesis of atherosclerosis.

Exercise has been a well-documented method to attenuate the rise in PPL (76, 83,

120, 168, 222) and prevent a detriment to cardiovascular function (27, 50, 61, 95, 160,

200). However, recent epidemiological evidence suggest that exercise training may not

be adequate to reduce incidences of disease and other morbidities, and even death in

people who spend a large amount of time sitting (70). Despite well-established (49) and

newly formed recommendations (39), exercise performed concomitant with these

recommendations may not be enough to overcome the detrimental effects of sedentary

time.

New evidence has emerged suggesting prolonged inactivity and sedentary time

may impede or eliminate the positive effects classically associated with exercise (38, 98).

In an investigation by Kim et al (98) a group of individuals who sat for >14 h/day for 4

days did not show the “classic” attenuation PPL that has been shown previously with 60

minutes of aerobic exercise on the fifth day (99, 183). These authors termed this

phenomenon “exercise resistance” because it seems exercise was unable to acutely

improve cardio-metabolic indicators of health (e.g.; PPL and relative fat oxidation). In

order to counteract this “exercise resistance” due to prolonged sitting higher levels of

physical activity throughout the day may be needed. In fact, some studies are now

pointing to the idea that breaking up sedentary time, independent of total moderate to

vigorous physical activity, may be able to attenuate PPL (38, 69, 153) and restore or

maintain endothelial function (127, 132, 176). It is also important to systematically

10

evaluate if this “exercise resistance” extends to other typical training adaptations besides

improved lipid tolerance.

Most of these studies have evaluated the effect of inactivity time on responses to

acute (e.g.; one bout) of exercise. Yet, to test a true “exercise resistance” phenomenon it

would be vital to assess training adaptations incurred over the periods of training than

longer than simply an acute exercise bout. Thus, it is imperative to determine if short-term

training adaptations (i.e.; after 5 bouts of exercise training over 9 days) are blunted in

participants who are also relatively inactive outside of training (i.e.; <5,000 daily steps).

The purpose of this study is to determine if cardiovascular and metabolic responses

to exercise are improved in individuals who participate in intense exercise training, yet

reduce daily steps below 5,000, over the course of 5 training bouts over 9 days compared

to a group following the same training protocol but are physically active (i.e.; >15,000

daily steps). We proposed examining the differences in physiological (as assessed via heart

rate, blood lactate, and NIRS) and metabolic (as assessed by postprandial triglyceride and

glucose responses) adaptations to short-term training between Low Step (LS) and High

Step (HS) treatments. We hypothesized LS would exhibit differences in both physiological

and metabolic adaptations to short-term training compared to HS.

Methods

Sixteen healthy, initially sedentary and untrained male (n= 8) and female (n=8)

participants were recruited and randomly assigned to two groups. Both groups completed

a training regimen administered under supervision of the investigators. Outside of said

exercise regimen, one group was physically active (n=8), taking 16,048 steps/day, and the

11

other group was sedentary (n=8), taking 4,767 steps/day. Both groups were asked to refrain

from any planned exercise outside of the experimental design. Participants were given

written and verbal description of all the procedures and measurements used in this study,

and written informed consent was obtained. The Institutional Review Board of the

University of Texas at Austin approved this study (ClinicalTrials.gov Identifier:

NCT03352063).

Experimental Design

The experimental design consisted 17 days with three distinct phases (see Figure

1). Days 1-3 (Pre-training) consisted of baseline or pre-training measures. Days 4-14

(Training) consisted of alternating days of training and rest days. The final three days, 15-

17, (Post-Training) consisted of repeating measurements taken in pre-testing phase.

Following informed consent and completion of a health history questionnaire, On

the first day participants visited the Human Performance Laboratory (HPL) for initial or

baseline high fat tolerance test (HFTT). One the second day determination of peak

oxygen uptake while cycling (VO2peak). The following day, D3, participants completed a

15-minute submaximal cycling test at 79% of VO2peak value. While the maximal and

submaximal tests were being conducted, participants wore the activity monitor for

familiarization purposes. This activity monitor (activPAL, PAL Technologies, Glasgow,

Scotland)) is small and noninvasive in nature, measuring roughly 2 in x 1in x 0.1in in size

and worn anteriorly on the thigh. The monitor was placed in a small rubber sheath and

12

attached via transparent film dressing. The activity monitor is not waterproof and cannot

be worn while showering. Participants were thus instructed to remove the device prior to

showering and were provided with the materials to change the dressing immediately after

showering once the area is dry. Therefore, aside from showering, the activity monitor was

worn continuously throughout the training phase (D4 - D14). After testing on D3,

participants were asked to refrain from any planned exercise and to begin adhering to the

prescribed daily step count.

After the first bout of exercise training on the evening of day 6, another HFTT

was performed on day 7 to evaluate responses an acute bout of exercise. Participants

continued the training regimen, exercising and resting on alternating days such that there

were five exercise sessions and four rest days in this training phase. All exercise bouts

were identical and consisted of a 20-minute cycling bout at 79% of the participant’s pre-

training VO2peak followed by 10 minutes of rest. Participants then completed two 5-

minute bouts at ~90% VO2peak with 5-minute rest intervals between each bout. This

exercise prescription is in line with, or exceeds, the current physical activity guidelines

published by the American Heart Association (AHA) and American College of Sports

Medicine (ACSM) for improvements in cardiovascular fitness (49).

In the post-training phase, participants completed a HFTT following the final bout

of exercise on the evening on D14. On the D15, participants completed another

submaximal test at the same duration and absolute work rate as the submaximal test

13

during the pre-testing phase (15-minute submaximal cycling test at 80% of VO2peak). On

the final day participants completed a post-training VO2peak test.

Dietary Control

During the course of the study participants were asked to eat to satiety, following

a diet standard in macronutrient breakdown (126). Also, participants logged all food

using the MyFitnessPal mobile application (MyFitnessPal, Inc.). Participants were asked

to consume the same foods on the day prior to each HFTT. On the evening prior to the

HFTT participants were given a low-fat meal to consume as fat in the previous meal can

affect the response to a high-fat test meal (42, 184).

High Fat Tolerance Test (HFTT)

On the morning of the HFTT, participants arrived at the HPL following a 12-hour

fast and having consumed 500 ml of water 1 hour prior to arrival. Prior to the HFTT,

participants have body mass was measured. After resting for 5-minutes, an intravenous

catheter was inserted into an antecubital vein. A resting blood sample was taken and 10-

minutes later, the HFTT test meal consisting of melted ice cream and heavy cream;

approximately 14.8 kcal/kg (0.8 g, 1.2 g, and 0.2 g/kg BW of carbohydrate, fat, and

protein, respectively) was consumed in 5-minutes. Blood samples were then taken hourly

for the next 6-hours.

14

Postprandial Substrate Oxidation

During the HFTT, expired gas was collected for determination of whole body

carbohydrate and lipid metabolism. Participants rested for 10-minutes in a seated

position, followed by 10-minutes of expired gas collection via meteorological balloons

performed at 0, 2, 4, and 6 hours. It has been previously demonstrated that inactivity

reduces whole body fat oxidation (98).

Energy expenditure and substrate oxidation were calculated from oxygen

consumption, carbon dioxide production, and respiratory exchange ratio (RER), energy

expenditure and substrate oxidation were calculated based on the methods of Lusk (118),

below.

% Energy from carbohydrate (CHO) oxidation = ((RER – 0.707)/0.293) x 100

% Energy from fat oxidation = 100 – % Energy from CHO oxidation

CHO oxidation (kcal/min) = (%CHO oxidation/100) x VO2) x 5.05kcal/L O2

Fat oxidation (kcal/min) = ((1-%CHO oxidation/100) x VO2) x 4.7kcal/L O2

Energy expenditure (kcal/min) = CHO oxidation + Fat oxidation

Maximal Oxygen Consumption Testing

During this procedure, participants breathed into a mouth-piece (while wearing a

nose-clip) that collected and analyzed the O2 and CO2 content of expired air. From this

participants oxygen consumption was determined and their peak value (VO2peak)

15

identified. The intensity of exercise, measured in watts, was increased every 1-2 min.

until they reached their maximal effort level and become fatigued. Volitional fatigue was

associated with a difficulty or inability to maintain cadence (>60 RPMs) while cycling.

The total length of the test was ~6-12 min, including a 4-minute warm-up. Heart rate was

also measured continuously from a strap worn around the chest (Suunto, Vantaa,

Finland). Heart rate data was used as a validation method for obtaining VO2peak.

Submaximal Exercise Testing

Submaximal exercise testing was conducted on a cycle ergometer and consisted

of a 15-minute bout at an intensity of ~80% of VO2peak derived from the VO2peak testing

described above. Blood samples were taken from an indwelling venous catheter at the

beginning and end of the 15 min submaximal exercise protocol to evaluate blood lactate

responses. Heart rate and VO2 were measured continuously, as described above. Near-

Infrared Spectroscopy (NIRS) (OxiplexTS, ISS Oximeter Model 95205, Champaign, IL)

was used to measure deoxygenated hemoglobin during exercise in the vastus lateralis, as

a final measure of physiological stress during submaximal testing. The acquisition

frequency of 2 Hz was used for this study. The data between 9 and 10 minute of the

testing protocol were averaged and recorded.

Biochemical Analysis

For plasma triglyceride and glucose concentrations, all blood samples collected

16

were immediately transferred to K2 EDTA collection tubes (BD Vacutainer, Franklin

Lakes, NJ), centrifuged at 3,000 g for 15 minutes at 4°C. Plasma was then stored in

separate aliquots at -80°C until later analysis. All measurements for each participant were

performed in duplicate within the same analysis. Plasma triglyceride and glucose

concentrations were measured by a spectrophotometric method using commercially

available kits (Pointe Scientific, Inc. Canton, USA).

For blood lactate concentrations, Blood samples were immediately deproteinized

by placing it in 8% perchloric acid and lactic acid levels were later measured on the

supernatant. Enzymatic analysis was used to determine blood lactate concentration based

on methods of Farrell et al (41). Intraassay coefficients of variation for plasma

triglyceride, glucose, and blood lactate concentrations were all less than 10%.

Statistical Analysis

Descriptive Statistics are reported as Mean SE. Descriptive statistics were

compared using students t-test (= 0.05). Differences in daily steps, maximal and

submaximal exercise responses, postprandial responses and incremental (AUCI) and total

(AUCT) areas under the curve for concentrations of plasma triglyceride and glucose were

determined by two-way ANOVA (Treatment X Time). Within group differences in

plasma triglyceride concentration and postprandial substrate oxidation were determined

using repeated measures two-way ANOVA (Trial X Time). Tukey’s LSD was performed

to determine if statistical significance exists. All data were analyzed using GraphPad

17

Prism 7 (GraphPad Software Inc., La Jolla, CA). The probability level for statistical

significance was sat at = 0.05.

Results

Participant Characteristics

Participants’ characteristics are described in Table 1. The total number of

participants was 16 (8 males, 8 females), with each participant randomly assigned to one

of the experimental conditions. Participants were generally young (23.6 4.7 years),

healthy individuals that were initially sedentary with similar VO2peak values (HS: 34.1

3.3; LS: 32.2 2.9 ml/kg/min, p>0.05). There were no differences in age (HS: 23.4. 5.6

yrs; LS: 23.8 4.0 yrs), height (HS: 166.4 7.9 cm; LS: 167.2 8.4 cm) or body mass

(HS: 74.4 0.1 kg; LS: 72.5 0.2 kg) between groups (p>0.05). Participant HR,

%VO2peak, RPE during exercise were all similar (p>0.05) and suggest exercise bouts that

could be classified as vigorous intensity (Table 2).

Daily Steps

Daily Steps (Figure 2) were recorded throughout the experimental design. A

significant main effect was found for treatment group. HS treatment group took

significantly more daily steps than LS group (HS: 16,048 725 steps/day; LS: 4767 376,

p<0.001). The groups adhered well to their prescribed step number. However, post hoc

analyses revealed that on D7, individuals in HS took significantly less steps than the same

group did on D12 and D14 (D7: 11,096 1361 steps/day; D12: 18,524 2481). These

differences most likely resulted from HFTT (i.e. required sitting for 6h) that occurred on

D7.

18

Total Plasma Area Under the Curve Responses

Plasma triglyceride concentrations were analyzed at each time point in all trials

for both treatments and calculated for incremental area under the curve (AUCI) and total

area under the curve (AUCT) (Figure 3). Plasma TG AUCT & AUCI showed significant

interactions (Treatment x Time, p< 0.05). Within the LS treatment group, no significant

differences were found between HFTT time points at Baseline, Acute or Post-training for

the AUCT or AUCI. Concurrently in HS, AUCT was significantly lower in both Acute

(760.9 73.7 mg/dL per 6 h, p< 0.01) and Post Training (762.2 65.5 mg/dL per 6 h,

p<0.01) as compared with those in Baseline (886.8 79.6 mg/dL per 6 h) with no

significant difference between Acute and Post Training (p>0.05). The incremental plasma

TG responses (TG AUCI) was significantly different in both Acute (221.7 49.7 mg/dL

per 6h, p<0.05) and Post Training (236.7 61.4 mg/dL per 6h, p<0.05) compared to

Baseline AUCI (322.9 67.2). Additionally, no differences were detected between

Acute and Post Training AUCI. Furthermore, plasma glucose AUCT and AUCI showed

no significant effects within, or between either treatment groups (p> 0.05) (Figure 4).

Overall, no between group differences reach statistical at Baseline, Acute or Post-

Training (p>0.05).

Postprandial Substrate Oxidation

19

Postprandial substrate oxidation was determined using indirect calorimetry (Table

3). Oxidation calculations were limited to 7 participants from each treatment group due to

possible hyperventilation at rest. Evaluation of postprandial RER data revealed

significant differences. Within HS, RERs during the HFTT were reduced after both

Acute (0.79 0.01) and Post Training (0.80 0.01) compared to Baseline (0.83 0.01,

p> 0.05). However, but no significant differences were found within LS or between

treatment groups. Likewise, Percent carbohydrate oxidation and percent fat oxidation

were found to have significant differences between trials in HS (p<0.05); while no

differences were seen within LS or between trials. Further, postprandial absolute fat

oxidation (i.e.; kcal*6h) was higher by 24% in Acute (p< 0.05) and 19% in Post Training

(p< 0.05) compared with that in Baseline. IN LS, there were no increases in absolute fat

oxidation during HFTT between Baseline or Acute or Post Training (p>0.05). Finally,

energy expenditures for the HFTT were not different within or between trials (p>0.05)

(Table 3).

Plasma Triglyceride & Glucose Concentrations

Plasma triglyceride and glucose concentrations (Figure 5 & 6) were analyzed at

Baseline, Acute and Post-training for both treatment groups and calculated for incremental

area under the curve (AUCI) and total area under the curve (AUCT). In LS, no significant

difference was found between trials at any time point for the six-hour triglyceride excursion

(Figure.6). However, in HS significant differences existed at several time points with

Acute and Post Training values compared to Baseline. Hour 1, 2 and 3 measurements were

significantly lower in both Acute and Post Training compared with Baseline (p<0.05). For

20

the last two measurements (e.g. H5, H6) of the Post Training HFTT were significantly

lower than baseline (p<0.05) with no differences between Acute and Baseline or Post

Training. No significant differences were found between trials at any time point for the six-

hour glucose excursion, between or within either treatment group (Figures 5 & 6).

Exercise Responses

Peak and submaximal exercise responses are summarized in Table 2. Peak oxygen

consumption (VO2peak) increased significantly from pre to post training (p<0.05). Oxygen

consumption and workload during submaximal exercise was similar between groups pre

and post training (p>0.05) and showed no difference within groups at either time point

(p>0.05).

Blood lactate concentration increased significantly with exercise (p<0.05), while

no differences existed between groups at rest (p>0.05) or during exercise (p>0.05). Pre-

intervention data showed no significant differences in HR, RPE, RER between groups

(p>0.05). After five bouts of exercise training, post-intervention testing revealed

significant reductions in HR and blood lactate concentration within HS (p<0.05), and no

significant changes in LS (p>0.05).

Furthermore, NIRS measurements at rest revealed no differences between groups

before or after the training (p>0.05). After training, deoxygenated Hb (HHb) was

significantly lower than pre-testing within the HS group (p<0.05). No significant

differences were found in LS pre vs post training (p>0.05).

Dietary Control

21

Daily caloric intake and percent of macronutrients were averaged across the 11-day

training phase. HS participants consumed 2389.1 153 kcal/day comprised of 50.7 0.3%

carbohydrate, 29.8 0.2% fat, & 19.5 0.2% protein. Daily caloric intake for LS averaged

1949.9 47 kcal/day comprised of 51.9 0.4% carbohydrate, 29.2 0.5% fat, & 18.9

0.2% protein. Caloric intake was significantly different between groups (p<0.001) No

differences existed between groups in percent macronutrient consumption (p>0.05).

Discussion

This study investigated the effect of 11 days of inactivity (<5,000 steps/day) on the

ability of a short-term exercise training regimen, consisting of 5 bouts over 9 days, to

improve postprandial triglyceride and classic markers of training adaptation. The foremost

finding was that, against a background of reduced daily steps (i.e.; low step; LS), intense

training (i.e.; 20-minute exercise bout at 80% VO2peak with two 5-minute intervals at 95%

VO2peak) failed to improve the postprandial metabolic responses to a high-fat meal or

promote classic whole body adaptations during submaximal exercise. This is noteworthy,

in that exercise of this intensity and duration was found to elicit sizable improvements in

high step (HS) as observed previously (46, 124, 183). In those who are active (e.g.; HS),

our findings suggest that a single acute bout of the prescribed exercise was effective in

lowering postprandial TG. Both AUCT and AUCI were significantly lower than baseline

after an acute bout of exercise, in the HS group accumulating ~16,000 daily steps.

Following four additional bouts of the same exercise postprandial metabolic responses

were similar to those seen after the single acute bout. Thus, the exercise was effective at

lowering PPL in an active group taking ~16,000 steps/day and additional bouts of training

22

appear to offer no greater benefit compared to a single bout. This agrees with previous

observations suggesting no additive effect of exercise bouts of consecutive days (40, 45).

Therefore, it seems that a reduced daily step count may somehow prevent or

severely encumber the healthy cardiometabolic adaptations that normally occur in response

to this training, both acute and chronically (46, 76, 77, 81, 83, 120, 221). This inability to

derive the protective effects of training, caused by physical inactivity, agrees with the

phenomenon of ‘exercise resistance’ first postulated by Kim et al (98). Further, this study

is the first to provide evidence that the implications of exercise resistance may extend to

short-term training.

While Kim et al (98) and one other study (1) also induced exercise resistance by

reducing daily steps to <2000 and <4000 steps/day, respectively, and used similar HFTT

testing procedures, these prior observations were limited to acute exercise bouts. In both

these studies a 1-h hour bout of exercise at ~65% VO2max failed to improve postprandial

metabolic responses, as would typically be seen following exercise of this duration and

intensity (76, 120, 167, 222). The present design contributes significantly in expanding the

consequences of daily physical inactivity beyond responses to a single bout of exertion into

a paradigm of more regular exercise that would fit within current recommendations (157).

Indeed, epidemiological studies have clearly documented that individuals who meet

current recommendations may not realize the reduced risk of CVD usually associated with

meeting guidelines, if these individuals are also inactive for the remainder of the day (16,

65). As others have noted (64, 65, 181, 205) it is possible, if not likely, that people living

in the US and similar industrialized countries could achieve or exceed physical activity

guidelines while still being inactive for 15 or more hours each day. It seems this interplay,

of prolonged inactivity and exercise, reduces the potency of the stimulus provided by

exercise training (1, 38, 98). However, there is a dearth of data on how the consequences

23

of this interplay would manifest themselves with even short term training as presently

employed. That is to say, while exercise resistance has been shown following acute

exercise, one might not expect the same results from a single bout of exercise to be derived

from short-term exercise training (96, 157). The findings from this investigation provide

evidence explaining, in part, the observations seen in previous epidemiological studies that

have found some individuals who are meeting published guidelines on physical activity are

not realizing reduced risk of CVD and premature mortality (16, 39, 150, 198, 218).

The findings from this study suggest individuals meeting current physical activity

guidelines may not derive the protective effect of daily physical activity to improve health,

at least in regard to postprandial triglycerides, if they are also experience extended periods

of inactivity. Our finding that, in a group of individuals taking 4767 steps/day, postprandial

responses were similar to untrained baseline whether individuals performed a single bout

of exercise or five exercise bouts is noteworthy. Further the exercise performed in this

study was a higher intensity (i.e. ~80-90% VO2peak) than in previous studies noting

exercise resistance (1, 98). This is significant in that higher intensity exercise has been

shown to exert greater PPL lowering effects than moderate, or low intensity (99, 183) and

may provide additional insight into the potency of the induced ‘exercise resistance’.

While our findings did not suggest an additive effect of the final four training bouts

for lowering PPL beyond the acute response to exercise, it is important to note that the

consistent exercise sessions should not be viewed as ineffective. It is probable that the TG

lowering effect of the training was exerted after each additional bout, not just after the first

and final bouts when HFTTs were performed, serving to maintain consistently-low daily

plasma TG levels. Regular exercise is likely to have beneficial effects on PPL through

short-term increases in LPL (164) as well as the other cardiovascular benefits generally

(149).

24

Among these cardiovascular benefits are indicators of muscular stress that are

typically reduced following exercise training while exercising at a given intensity. These

measures such as heart rate and blood lactate accumulation seem to be similarly unaffected

in LS compared to HS. While our findings clearly demonstrate that both conditions (high

and low step count) benefited from training, by increasing their VO2peak and thereby

decreasing the relative percentage of VO2peak needed to sustain an absolute work rate

while cycling, it seems adaptations at the level of the muscle (i.e.; blood lactate) may have

been impaired. It is unlikely that the changes seen in HS were due to the higher number of

steps causing an additional training effect beyond that provided by the intense training (80-

90%VO2peak), but that possibility can’t be discounted. Individual who simply increase

their daily step count over several weeks, typically don’t increase VO2max or show the

adaptations to submaximal exercise currently seen in HS (170).This study could have

benefitted from an additional control group in which participants maintained a high step

count but did not participate in the short-term training. This would have allowed isolation

of the effects of increasing daily activity alone. While is it probable that this level of daily

walking, in HS, was somewhat higher than the participants would experience during

normal daily living, it is highly unlikely that the intensity of walking (e.g. ~30% VO2max)

elicited significant adaptation (212). This suggests the lack of improvement in blood lactate

in LS pre-vs-post training may be due to inactivity producing an intramuscular

environment that might be ‘resistant’ to the stimulus provided by training.

Reductions in daily steps, whether imposed by prolonged sitting or another form of

induced inactivity may lead to a condition in which uptake of substrates in the blood by

muscle is reduced. While this requires speculation, it is possible that the similar levels of

blood lactate, plasma triglycerides, and plasma glucose where due to decreases in cellular

expression or activity of membrane transporters such as MCT, GLUT4, and GPIHBP1

25

proteins responsible for increased uptake of these energy substrates. Disuse, modeled

through denervation of a rats hindlimb resulted in decreases in MCT1 in the soleous and

MCT4 in the gastrocnemius (210). Because muscle serves as a primary consumer of lactate

during exercise, any decreased expression of MCTs could lead to increased lactate

concentrations during steady state exercise, or the lack of improvement blood lactate levels

seen in LS. Similarly It has been suggested that hindlimb suspension could result in

decreases in GLUT4 expression at the surface of the sarcolemma (94).

Recent observations support this contention in that not only is the postprandial

triglyceride response impaired with inactivity but a plethora of other metabolic responses

may be diminished or abolished as well (1, 13, 38). Consistent with our findings,

Bergouignan et al. (13) showed that 32 days of bed rest increased PPL by 27%, compared

to an ambulatory control, and this effect was not averted by exercise training performed

every 3 days during the bed rest. Duvivier et al. (38) showed an hour of exercise was not

sufficient to counteract the effects of sitting for 13 hours which resulted in no significant

improvements in triglycerides, non-HDL cholesterol and apolipoprotein B plasma levels

compared to a sitting condition without exercise. Interesting a group with matched energy

expenditure through increased daily walking did see improvements in each of those

measures, compared to sitting plus exercise, without structured exercise(38). Further

reduced myofibrillar protein synthesis can be seen in elderly (22) and young healthy

individuals (166) in response to step reductions of two weeks or less. This could be due to

reduced uptake of amino acids from circulation, similar to the reduced plasma triglyceride

and plasma glucose uptake shown by this study and others (1, 13, 98).

Primary amongst the limitations of the current study was a lack of sufficient power.

Power analyses indicated that a sample size of approximately 38 individuals would be

necessary to detect between groups differences in triglyceride AUCI. Despite sizable

26

differences in postprandial triglyceride responses within the respective treatments, it is

likely that this lack of power prevented our ability to detect difference between our

treatment groups, both in postprandial and exercise responses. Therefore, while it can be

concluded that the short term exercise training significantly improved PPL both acutely

and chronically in HS, whereas it did not significantly improve PPL in LS, it cannot be

concluded that HS was found to be significantly better than LS.

Secondly, it is possible that our results were influenced by selection bias.

Participants were all previously sedentary, and were randomly assigned to each ‘step’

group. It is unclear if that those who participated in the study are fully representative of a

broader sedentary population, as they sought out an opportunity to participate in intense

exercise testing and training and were willing to commit to and strictly follow a structured

program of that nature.

Participants in the study, especially within LS, also began to wane from strict

adherence to the daily step protocol as the training phase progressed. Due to the nature of

the study, extending 11 days across 2-3 weekends, to carefully ensure adherence to the

assigned daily step goal would have caused an undue burden on the participants in this

study by removing them from their normal routine. Having participants remain in the

laboratory or under direct observation is impractical for the scope of this work. Despite

taking more than 6,000 steps/day on a few days prior to the ‘Post Training’ HFTT (D10,

D12, and D13), the relative increase in daily steps seems to have little effect on the

postprandial responses. This is evidenced by similar AUCI during ‘Acute’ and ‘Post

Training’ HFTTs while taking less than 4,400 step/day, on average, in the days preceding

the ‘Acute’ HFTT. This may indicate that 6,000 steps/day is also inadequate to counteract

‘exercise resistance’ and realize the protective effects of exercise training, at least in terms

of PPL. Currently, however, this requires speculation as ‘exercise resistance’ has only

27

recently been recognized (98)) and the nature of its development and abatement have yet

to be fully described. That is, we cannot say conclusively that 6,000 step/day results in the

development exercise resistance in the absence of prior days of an even further reduced

daily step count as presented here. Further work is needed to address these issues

definitively.

In conclusion, the data presented here suggest that 11 days of step reduction (i.e.;

LS; 4,767 steps/day) prevents the improvements in PPL typically seen following intense

exercise training when background step count is high (e.g.; HS: 16,048 steps/day). Instead

of reductions in TG AUCs, as seen with training in HS, the results indicate no

improvements compared to baseline after acute exercise or short-term training. The finding

from the current investigation indicate that reducing steps below approximately 5,000

steps/day may generate reduced responsiveness to normal, healthy stimuli of intense short-

term exercise training. These findings suggest that reliance on exercise may not be enough

to sustain a low PPL in those whose lifestyles are characterized by regular, prolonged

inactivity.

28

Tables and Figures

Figure 1. Study Design. Participants were separated into two groups (High Step or Low

Step) and completed a short-term exercise regime with physiological and metabolic

testing pre and post training. Subjects took their assigned step number on days 4-14.

29

Figure 2. Daily steps were measured via activPal activity monitor, attached on the

participant’s anterior thigh throughout each trial. Average daily step count, for each

group are presented for the 11-day intervention period (D4-D14). Average daily steps

were significantly different between groups for every day measured (p<0.001). (*)

significantly different from D12 and D14 within treatment group (p<0.05).

D4 D5 D6 D7 D8 D9 D10 D11 D12 D13 D140

5000

10000

15000

20000

25000

Day of Trial

Nu

mb

er o

f S

tep

s

Step Data Low Step

High Step

*

30

Figure 3. Total and Incremental areas under the curve of plasma triglyceride

concentration during HFTT at Baseline, following a single bout of exercise (Acute), and

following 5 training bouts over the 9-days of training (Post Training). (*) Significantly

different from Baseline within treatment group, p<0.05. (†) significantly different from

Baseline within treatment group, p<0.01. Data reported as meanSE.

Baseline Acute Post Training0

500

1000

1500

Tri

gly

ceri

de (

mg

/dL

*6h

)

Plasma Triglyceride Total Area Under the Curve

High Step

Low Step

† †

Baseline Acute Post Training0

100

200

300

400

Tri

gly

ceri

de (

mg

/dL

*6h

)

Plasma Triglyceride Incremental Area Under the Curve

High Step

Low Step

* *

31

Figure 4. Total and Incremental areas under the curve of plasma glucose concentration

during HFTT at Baseline, following a single bout of exercise (Acute), and following 5

training bouts over the 9-days of training (Post Training). Data reported as meanSE.

Baseline Acute Post Training0

200

400

600

800

1000

Glu

co

se (

mg

/dL

*6h

)Plasma Glucose Total Area Under the Curve

High Step

Low Step

Baseline Acute Post Training0

50

100

150

200

250

Glu

co

se (

mg

/dL

*6h

)

Plasma Glucose Incremental Area Under the Curve

High Step

Low Step

32

Figure 5. Temporal responses of plasma triglyceride & plasma glucose concentrations

for High Step treatment during the HFTT at Baseline, following a single bout of exercise

(Acute), and following 5 training bouts over the 9-days of training (Post Training). (*)

Acute significantly different from Baseline, p< 0.05. (†) Acute & Post Training

significantly different from Baseline, p< 0.01. (#) Post Training significantly different

from Baseline, p<0.05. Data reported as meanSE.

0 1 2 3 4 5 650

100

150

200

Time (Hours Postprandial)

Tri

gly

ceri

de

(mg/d

L)

Acute

Baseline

Post Training

High Step Plasma Triglyceride Response

†

**

##

0 1 2 3 4 5 680

100

120

140

160

Time (Hours Postprandial)

Glu

co

se (

mg

/dL

) Acute

Baseline

Post Training

High Step Plasma Glucose Response

33

Figure 6. Temporal Responses of plasma triglyceride & glucose concentrations for Low

Step treatment during HFTT at Baseline, following a single bout of exercise (Acute), and

following 5 training bouts over the 9-days of intervention (Post Training). Data reported

as meanSE.

0 1 2 3 4 5 650

100

150

200

Time (Hours Postprandial)

Tri

gly

ceri

de

(mg/d

L) Acute

Baseline

Post Training

Low Step Plasma Triglyceride Response

0 1 2 3 4 5 680

100

120

140

160

Time (Hours Postprandial)

Glu

co

se (

mg

/dL

)

Acute

Baseline

Post Training

Low Step Plasma Glucose Response

34

Physical Characteristics High Step (n=8) Low Step (n=8)

M/F 4/4 4/4

Age (y) 23.4 2.0 23.8 1.4

Height (cm) 166.4 2.8 167.2 3.0

Body Mass (kg) 74.4 5.9 72.6 3.9

BMI (kg/m2) 26.7 1.9 25.9 1.0

Note: Data are reported as MeanSE

Table 1. Descriptive statistics of the two groups (i.e. High Step and Low Step) at the

beginning of the study. All data are reported as mean SE.

35

High Step Low Step

Pre Post Pre Post

Absolute VO2peak (L/min) 2.51 0.3 2.70 0.2† 2.35 0.2 2.52 0.3*

Relative VO2peak (mL/kg/min)

34.1 ± 3.3 36.9 3.6† 32.2 ± 2.9 34.5 3.3*

Submaximal VO2

(L/min) 1.98 ± 0.7 1.98 0.9 1.87 ± 0.6 1.88 0.2

%VO2peak 78.3 0.8 72.5 1.8† 79.9 0.7 75.3 1.3*

Heart Rate (bpm)

181.4 ± 4.7 168.8 4.6† 180.9 5.3 175.8 4.1

HHb (AU)

21.7 4.6 20.1 4.1* 21.1 6.5 22.1 6.3

Blood Lactate

Concentration (mmoL)

7.6 0.8 6.7 0.8* 7.2 0.3 7.2 0.4

RPE 15.6 ± 0.8 13.8 0.5 15.8 0.4 14.5 0.6

Workload (W)

135.1 18.7 -- 125.6 15.3 --

Note: Data are reported as MeanSE. (*) Significantly different from Pre, p<0.05. (†)

Significantly different from Pre, p<0.01

Table 2. Physiological responses to maximal and submaximal exercise testing. (*)

significantly different from pre-testing within treatment group, p<0.05. (†) significantly

different from pre-testing within treatment group, p<0.01. All data are reported as mean

SE.

36

Variables High Low

Baseline Acute Post Training Baseline Acute Post Training

RER 0.83 0.01 0.79 0.01* 0.80 0.01* 0.83 0.01 0.82 0.01 0.81 0.01

Fat Oxidation (%) 58.5 3.18 70.7 2.32* 67.3 2.10* 57.7 3.89 62.6 2.81 63.9 2.57

Fat Oxidation (kcal/6h) 310.2 18.2 384.1 25.1* 368.9 16.5* 330.4 37.2 351.8 43.4 350.7 29.2

CHO Oxidation (%) 41.5 3.18 29.3 2.32* 32.7 2.10* 42.3 3.89 37.4 2.81 36.1 2.57

CHO Oxidation

(kcal/6h) 216.6 19.1 157.8 11.9 181.2 13.7 233.9 22.5 203.5 11.3 201.1 23.1

Total Energy

Expenditure (kcal/6h) 526.8 18.7 541.9 26.3 550.1 18.6 564.3 39.6 555.3 45.2 551.8 43.9

Note: Data are reported as Mean SE.

Table 3. Overall postprandial substrate oxidation during HFTTs at Baseline, following a

single bout of exercise (Acute), and following 5 training bouts over the 9-days of training

(Post Training). (*) Significantly different from Baseline, (p<0.05). Data reported as

meanSE.

37

Chapter IV: Study #2

DOSE RESPONSE OF PHYSICAL INACTIVITY ON PLASMA TRIGLYCERIDES AFTER A

MEAL

Abstract

Background: It has been suggested that there is a linear, inverse dose-response

relationship between the daily steps and cardiovascular events. However, it seems if

individuals severely reduced the number of steps taken throughout the day the protective

effects of exercise may not be realized. The objective of this study was to determine

differences in postprandial metabolic responses following acute exercise against a

background of differing levels of daily step reduction.

Methods: Ten participants completed three, five-day trials in a randomized, crossover

design with differing levels of daily step reduction. Following two days of controlled

activity, participants completed two days of Low, Limited, or Normal Activity (2,675,

4,759, or 8,480 steps/day, respectively). Participants also completed a one-hour bout of

exercise on the evening of the second day of step reduction. A high fat tolerance test was

performed on the following morning. Postprandial responses were compared in each trial.

Results: Daily steps were significantly different in each trial (2,675, 4,759, or 8,480

steps/day, respectively; p<0.05) while responses to the acute moderate intensity exercise

were similar (p>0.05). Following the NORM trial, participants’ incremental plasma

triglyceride response was lower than LIM by 23% (p<0.05) and LOW by 22% (p<0.05).

Whole body fat oxidation was also significantly increased in NORM compared to the two

other trials (p<0.05). No significant differences were found between LIM and LOW in any

postprandial measure.

Conclusion: In conclusion, two days of daily step reduction in young healthy individuals

can impair the ability of acute exercise to attenuate PPL. The finding that the participants

38

don’t lower PPL or increase fat oxidation in response to exercise when taking ~4700 steps

or less, may indicate a reduced responsiveness of skeletal muscle to exercise. This agrees

with the newfound phenomenon of ‘exercise resistance’ in individuals whose daily life is

characterized by inactivity (e.g. prolonged sitting) and low step count.

39

Introduction

The cardio-metabolic health benefits of physical activity and exercise such as

improved postprandial hypertriglyceridemia and improved glucose tolerance, can be

gained acutely from a single bout of exercise and lost with several days of inactivity (80,

82, 83). However, studies in which acute exercise resulted in reduced postprandial

hypertriglyceridemia, the participants were accumulating approximately 7,000-8,500

steps on the day before evaluation of postprandial metabolism (99, 183, 184). Although,

in a recent study Kim et al. (98), the authors reported that in participants who were sitting

for >14 h/day and taking only 1,650 steps/day, a one-hour bout of running at 67%

maximal oxygen consumption (VO2max) failed to improve postprandial

hypertriglyceridemia the next morning. It seems that physical inactivity (i.e; severely

reduced step count) rendered the participants resistant to the normal acute improvements

in indices of cardio-metabolic health that are normally derived from a one-hour bout of

running. This phenomenon is referred to as ‘exercise resistance’ (98). A follow-up study

in 2019 (1), used additional controls to verify the existence of this phenomenon found a

group taking ~3700 steps/day also exhibited exercise resistance. Therefore, it is important

to systematically delineate what magnitude of daily step reduction causes impairment of

the ability of acute exercise to improve the postprandial plasma triglyceride response.

In modern culture, we have engineered physical activity out of our daily lives.

Periods of prolonged inactivity, characterized by mostly sitting, have become routine in

the lives of many and routinely coincide with the non-fasting or postprandial state. In the

postprandial state, triglyceride levels in the plasma can remain elevated for up to 10

40

hours, typically peaking 3 - 6 hours after a meal rich in fat (152). The magnitude and

duration of this elevation is influenced by prior physical activity (52, 120, 221), diet

(174), and genetics (173, 204). As demonstrated in recent epidemiological studies (5,

142), non-fasting plasma triglyceride levels, i.e., post-prandial lipemia (PPL), better

predicts cardiovascular events than fasting plasma triglyceride levels and are known to be

associated with diseases, including metabolic syndrome, type 2 diabetes, and

atherosclerosis. In fact, several recent epidemiological studies, inactivity and/or sitting

time has been strongly associated with the risk of obesity, metabolic disorders including

type 2 diabetes mellitus and especially with cardiovascular disease and death (16, 198,

218). Surprisingly, some have reported that the risks from prolonged sitting appears

“independent” of the volume of exercise being performed (16, 150, 198, 218). This

means people who meet the recommended guidelines (AHA or ACSM) for physical

activity of 150 min/week of moderate intensity exercise appear to still be at risk for

developing cardiovascular disease and all-cause death if they have a lifestyle routinely

incorporating prolonged periods of inactive, sedentary behavior (>10-12 h/day).

One of the strongest negative correlations (r=-0.96) in relation to sedentary time is

time in light-intensity activity, such as walking (65). This means increasing time spent

ambulatory reduces sitting time. Accordingly, manipulation of daily step count has been a

popular and potent method of studying the effects of inactivity. Appropriately, increased

daily walking has been shown to reduce cardiovascular events (122) while reductions in

daily step number for as little as one week have been associated with drastic increases the

area under the curve of plasma insulin during an oral glucose tolerance test (OGTT)

41

(145). This increase showed the potential to grow to nearly 80% greater, if the reductions

were maintained for 2 more weeks (145). Daily step reductions have also been linked to

decreased VO2max, endothelial dysfunction, decreased insulin sensitivity, decreased lean

leg mass and increased abdominal fat (22, 109, 145).

Meanwhile, it is well established that a single bout of moderate exercise lasting

60-90 minutes attenuates PPL regardless of prior lipid levels (221) and training status

(66, 120, 151). Many studies (52, 120, 221) have been conducted to investigate the effect

of a single bout of moderate intensity exercise on postprandial triglyceride levels in

comparison to a control condition. Participants in these studies were asked to refrain from

any planned exercise but their ambulatory activity, including walking, was not carefully

controlled. Furthermore, very few studies (1, 98) have investigated the collective effect of

daily step reduction and moderate exercise. Although recent data (1) present compelling

evidence that drastically reducing daily step number may abolish the ability of an acute

bout of exercise to attenuate the increase in PPL, some other studies seem to suggest this

may not occur with as little as ~7,900 steps/day (183).

Thus, the purpose of this study was to investigate the effect of reductions in daily

step number and a single 1h bout of moderate intensity exercise on postprandial

concentrations of plasma triglyceride and glucose, as well as fat oxidation. We

hypothesized that postprandial responses, following a single bout of 1-hour of running at

65% VO2max, would differ as daily steps increased.

42

Methods

Ten healthy untrained, recreationally active male (n=7) and female (n=3)

participants completed three different trials of differing daily step counts based on

previously established cut-points for physical activity (194). Participants were assigned to

Low Activity (LOW): 2675 steps/day, Limited Activity (LIM): 4,759, and Normal Activity

(NORM): 8480 steps/day in a randomized, crossover design, each occurring over five days

with at least a week interval between trials (See Figure 7). Participants were asked to refrain

from any planned exercise outside of the experimental design. Participants were given

written and verbal description of all the procedures and measurements used in this study,

and written informed consent was obtained. The Institutional Review Board of the

University of Texas at Austin approved this study (ClinicalTrials.gov Identifier:

NCT03697382).

Experimental Design

Each trial consisted of three phases: the first two days served as a control phase

(C1 and C2), that allowed for familiarization and control, followed by a 2-day

intervention phase consisting Low, Limited, or Normal physical activity consisting of

daily step counts of 2,675, 4,759, or 8,480 steps per day, respectively, with 1-h of running

on the evening of the second day of each trial at 64% VO2max on a laboratory treadmill.

On the morning of Day 3 all participants ingested a high fat shake (i.e.; high fat tolerance

tests; HFTT) and the postprandial responses were measured over the subsequent 6h

period. Throughout the three trials, participants were instructed to refrain from any

43

exercise other than that prescribed in the study design. Participants were also asked to

keep a consistent sleep/wake cycle during the trials.

Preliminary Testing

One week prior to the initiation of the first trial, participants visited the Human

Performance Laboratory (HPL) for a 20-min, 4-stage submaximal test to determine

oxygen consumption while jogging at different paces followed by determination of

maximal oxygen uptake (VO2max). This served to determine the appropriate treadmill

speed to elicit the desired intensity during the 1-h exercise bout. In order to determine

VO2max participants performed an incremental treadmill test lasting 8-12 minutes during

which the incline was increased 2% every 2 minutes (29). VO2, VCO2, and heart rate

were monitored throughout the test, and the highest 30 second VO2 average was recorded

for the participant’s maximal oxygen consumption. The ACSM criteria for VO2max was

used in assessing a successful VO2max test. These criteria are: a plateau in oxygen

consumption (less than 150 ml/min increase in VO2 with increasing work), respiratory

exchange ratio (RER) >1.1, maximal heart rate within 10 bpm of predicted maximal heart

rate, and a rating of perceived exertion (RPE) of 17 or greater.

Control Phase

44

Participants were instrumented with an activity monitor worn on their thigh to

record step count (activPAL, PAL Technologies, Glasgow, Scotland) and the monitor

began recording at 0:00hrs on the first day of the control phase (C1). Participants were

asked to remain aware of their step count and to limit steps to 8000 or less to approximate

a non-sedentary, low level of physical activity (189). If participants were unable to

achieve 8,000 step limit in their first trial, they were the asked to repeat their activity as

closely as possible during the control phases of the subsequent trials.

Intervention Phase

During the intervention phase, D1 & D2, participants were asked to remain seated

or lying for much of the day to accommodate their assigned level of non-exercise activity

(2,675, 4,759, or 8,480 steps/day). On D2 of each trial participants continued to adhere to

the assigned step count, but completed a 1-h run at 64.4% VO2max at 18:00h. The steps

during this bout of exercise were not included as part of the participants total for D2.

High Fat Tolerance Test (HFTT)

Participants were given a low fat meal the evening prior to high fat tolerance test

(HFTT) given that the plasma TG response to a high fat shake (HFS) may be affected by

the fat content of a previous meal (42). On the day of the HFTT (D5), participants

reported to the laboratory at 07:00 h. Body weight was measured. They then lie on a

padded table for 5 minutes before insertion of a catheter into an antecubital vein. A

45

fasting blood sample was collected 10 min before consumption of a high fat shake (HFS)

(mostly melted ice cream and heavy cream; approximately 14.8 kcal/kg (0.8 g, 1.2 g, and

0.2 g/kg BW of carbohydrate, fat, and protein, respectively). Participants were asked to

consume the HFS in 5 minutes. Blood samples were collected over the next 6 hours at 0,

2, 3, 4 and 6h post consumption of the HFS. All blood samples collected were transferred

to K2EDTA collection tubes (BD), centrifuged at 2,000 g for 15 minutes at 4◦C and then

stored in -80◦C freezer until later analysis. During HFTT, participants were asked to

remain seated quietly reading, watching movies, and/or surfing the internet. Participants

were allowed to

Postprandial Substrate Oxidation

Postprandial expired gas collection was used for indirectly assessing substrate

oxidation. Participants rested in a chair for 10 minutes, followed by expired gas

collection through meteorological balloons for 10 minutes at 0, 2, 4, and 6 h. It has been

previously demonstrated that inactivity reduces whole body fat oxidation (98).

Energy expenditure and substrate oxidation were calculated from oxygen

consumption, carbon dioxide production, and respiratory exchange ratio (RER), energy

expenditure and substrate oxidation were calculated based on the methods of Lusk (118).

% Energy from carbohydrate (CHO) oxidation = ((RER – 0.707)/0.293) x 100

% Energy from fat oxidation = 100 – % Energy from CHO oxidation

CHO oxidation (kcal/min) = (%CHO oxidation/100) x VO2) x 5.05kcal/L O2

46

Fat oxidation (kcal/min) = ((1-%CHO oxidation/100) x VO2) x 4.7kcal/L O2

Energy expenditure (kcal/min) = CHO oxidation + Fat oxidation

Dietary Control

During the course of the study participants were asked to eat to satiety.

Participants logged all food and were asked to consume the same foods on the day prior

to each HFTT. On the evening prior to the HFTT participants were given a low-fat meal

to consume as fat in the previous meal can affect the response to a high-fat test meal (42,

184). Participants were allowed to supplement higher energy expenditure during the LIM

and NORM step trials with a small snack but were asked to adhere to a diet standard in

macronutrient breakdown (126).

Biochemical Analysis

For plasma triglyceride and glucose concentrations, all blood samples collected

were immediately transferred to K2 EDTA collection tubes (BD Vacutainer, Franklin

Lakes, NJ), centrifuged at 3,000 g for 15 minutes at 4°C. Plasma was then stored in

separate aliquots at -80°C until later analysis. All measurements for each participant were

performed in duplicate within the same analysis. Plasma triglyceride and glucose

concentrations were measured by a spectrophotometric method using commercially

available kits (Pointe Scientific, Inc. Canton, USA). Intraassay coefficients of variation

for plasma triglyceride and glucose concentrations were all less than 10%.

47

Statistical Analysis

Incremental (AUCI) and total area under the curve (AUCT) for plasma triglyceride

and glucose were calculated. Once calculated, repeated measures one-way analysis of

variance (ANOVA) was used to test for differences. Plasma glucose and triglyceride curves

were calculated and analyzed using repeated measures two-way ANOVA (trial x time).

Daily step counts were analyzed using repeated measures two-way ANOVA (trial x time).

Similarly, respiratory exchange ratio (RER), as well as fat and carbohydrate oxidation,

were analyzed using repeated measure two-way ANOVA (trial x time). When interactions

were significant, Tukey’s honestly significant difference post hoc tests were run. All data

were analyzed using GraphPad Prism 7 (GraphPad Software Inc., La Jolla, CA). All data

are expressed as mean standard error of the mean (SE), unless otherwise noted, the level

for statistical significance was set at p 0.05.

Results

Participant Characteristics

Participant characteristics are summarized in Table 4. A total of 10 participants

were recruited (7 males, 3 females), with all participants completing all three trials.

Participants were apparently healthy, young adults (24.0 ± 1.8) that were untrained to

recreationally active.

Responses to Maximal and Submaximal Exercise

Responses to maximal and submaximal treadmill running are shown in Table 5.

Submaximal exercise bouts elicited a heart rate of 153.9 3.9 bpm and an oxygen

48

consumption of 2210.2 154 ml/min, which equated to approximately 64.4% of

participants VO2max. These responses were indicative of moderate intensity exercise and

were not different between trials.

Daily Steps

Daily steps are presented in Table 6. A significant Trial x Time interaction was

found daily step count (p<0.001). Post hoc analyses revealed no significant differences

within or between trials for control days (p>0.05). However, daily steps on Day 1 of the

intervention were significantly different for all trials (LOW: 2,744331, LIM: 4,482318,

NORM: 8,431732). Likewise, during Day 2 of the intervention, excluding exercise steps,

daily steps were significantly different for all trials (LOW: 2,605313, LIM: 5,037206,

NORM: 8,530420; p<0.01 for all comparisons).

Total Plasma Area Under the Curve Responses

Plasma triglyceride concentrations were analyzed at each time point in all trials

for all trials and calculated for incremental area under the curve (AUCI) and total area

under the curve (AUCT) (Figure 8). Analysis of plasma TG AUCT revealed significant

differences with NORM being significantly lower than LOW (p<0.01) and a trend toward

difference compared to LIM (p=0.09). AUCI in NORM (267.539.2 mg/dL*6h) was

significantly different from LOW and LIM (342.347.8 and 348.648.9 mg/dL*6h,

respectively, p<0.05). No differences were detected between LOW and LIM in AUCT,

49

nor AUCI. Plasma glucose areas under the curve showed no differences (p>0.05)

between the three trials in AUCT or AUCI (Figure 9).

Plasma Triglyceride & Glucose Concentrations

Plasma triglyceride and glucose concentrations were analyzed at each time point in

all trials for both treatments. Triglyceride and glucose excursions are shown in Figure 10

for TG, and Figure 11 for glucose. Significant differences existed at multiple time points

between trials. At hours 2 and 3, NORM was significantly different from LOW (p<0.05).

At hour 3, NORM was also significantly different from both LIM (p<0.05). NORM was

also significantly different from LOW (p<0.01) and LIM (p<0.05) at hour 4. No differences

existed at baseline or hour 6 for triglyceride concentrations (p>0.05). Furthermore, no

significant differences were found between trials at any time point for the six-hour

triglyceride excursion.

Postprandial Substrate Oxidation

Postprandial substrate oxidation was determined using indirect calorimetry (Table

8). Postprandial RER was significantly different in NORM (0.770.01) compared to LOW

(0.800.01, p<0.05) with a strong trend toward significant from LIM (0.81 0.01, p=0.06).

Similarly, percent fat and carbohydrate oxidation, as well as absolute carbohydrate

oxidation (i.e. kcal*6h) were different in NORM compared with LOW (p<0.05). Notably,

Absolute fat oxidation was significant different in NORM (396.027.5 kcal), compared

with LOW (318.934.5 kcal, p<0.05) and LIM (342.430.9, p<0.05).

50

Post hoc analysis for bihourly RER measurements (Appendix F) revealed

differences for hour 2 between NORM and both LOW and LIM (p<0.05), while RER was

similar between trials at other measurement time points (p>0.05). Similarly, differences in

percent fat oxidation and percent carbohydrate oxidation were significant at hour 2. Finally,

no difference was found between trials in overall postprandial energy expenditure, or

energy expenditure at any single time point between or within trials (p>0.05).

Discussion

The purpose of this study was to investigate the effect of daily step reductions on

postprandial responses to a high fat meal the morning after an acute bout of moderate-

intensity exercise. We hypothesized our key measures, TG AUCI, and fat oxidation would

display a curvilinear dose-response relationship with daily steps taken in the two days

preceding an acute exercise bout. The primary finding of this study was that when

individuals take ~8500 daily steps their postprandial triglyceride responses and whole body

fat oxidation during a HFTT, following 1-hour of exercise at 64% VO2max, the night before

were significantly improved compared with the same individuals taking 4,759 steps/day or

2,675. In this randomized, cross over experimental design individuals displayed a 23% and

22% reduction in TG AUCI when averaging 8480 steps/day, compared to those same

individuals when taking 4,759 or 2,675 steps/day, respectively. The reduction in plasma

TG concentration may be due to an increased uptake by tissue and increased oxidation,

which was also significantly increased in NORM compared to the other trials.

This is a striking difference as reductions in TG AUCI after similar exercise has

been shown to induce TG AUCI reductions on the order of 20-40%, compared to a non-

exercise control (46, 99, 183, 221). In other words, when taking 8480 steps/day (i.e.;

51

NORM), the observed reductions in TG AUCI, compared to both of the LOW and LIM

trials, were similar to reductions typically seen compared to a “no exercise” condition.

Despite completing identical exercise bouts, the night before commencement of the HFTT,

participants seem to have displayed decreased responsiveness to said exercise if they

reduced daily steps below ~4,700 steps/day; at least in regards to PPL. Olsen et al (145)

found simply reducing steps from ~10,500 to ~1,400 steps/day for two weeks increased

TG AUCT by 21% in the absence of exercise.

Recent research has begun to place a particular emphasis on the benefits of

increases in daily step counts (107, 111, 189, 191). This is probably due to the ease of

translation as a step/day metric is easy to understand and practical, thanks to technology

such as wearables that increase the ease of self-monitoring effectively and affordably.

Some have recently proposed an inverse dose-response between daily step counts and

incidence of cardiovascular disease, type 2 diabetes, and all-cause mortality (107) (111).

Lee et al. (111) found rates of mortality progressively declined with increasing daily steps

until plateauing at approximately 7,500 steps/day. Contrary to the findings in the present

study, the authors reported groups taking as few at 4,363 steps/day displayed reduced

mortality compared those taking 2,718 step/day. The difference in findings may be due to

the fact that the participants in Lee et al. were substantially older than the population

recruited for the current study. Moreover, a recent meta-analysis (107) suggested a 10%

reduced risk of cardiovascular events for each 2,000 step increase in daily step number up

to 10,000 steps/day. These investigations differ from the findings in this study, as we did

not find any improvement in postprandial responses when an individual increased daily

walking from ~ 2,700 to ~ 4,750 steps/day. A few important distinctions should be noted

and may explain this discrepancy. First, and most obviously, the current investigation

focused on responses following moderate exercise which was not employed in the

52

aforementioned studies. Secondly, the “baseline” step counts in almost all of these

investigations, and given as a hypothetical baseline within one (107), exceeded 5,000

steps/day (107, 111, 216) which excludes comparisons below this level of daily walking,

such as the 2675 step/day trial presented herein. The current investigation was also

conducted over a much shorter time period than the observations. Lastly PPL, while

indicative of CVD events, is only a single factor contributing to the development of CVD

and should not be considered equivalent or wholly indicative of CVD.

It is possible that at severely reduced step counts (<5,000 steps/day) physiological

and metabolic responses differ from those above ~8,000 steps/day. This seems to be

buttressed by a growing public health literature that suggest a daily step count at or below

5,000 should be classified as a “sedentary lifestyle index” (189, 191) and should be viewed

as a problematic because of the distinct health ramifications seen below this level of

activity due to “non-exercise activity deficiency” (64). Though this is a reduction below

the level some would consider ‘normal’, it should not be ignored. In fact estimates from

the NHANES study, based on objectively collected accelerometer data, indicate

approximately 37% of the US population would fall below this level of daily activity (194).

Our findings are particularly interesting in light of recent findings of a phenomenon

termed “exercise resistance” (1, 98). These authors observed individuals taking less than

4,000 daily steps were resistant to the exercise stimulus provided by 1-h of running at ~65%

VO2max. In these randomized crossover trials (1, 38, 98) the protective effects of exercise,

preventing exaggerated rises in postprandial plasma triglycerides and glucose, were not

realized if daily step counts were reduced by imposed sitting of 13 hours or more. It seems

by drastically reducing the contractile activity in the study participants, an environment

was produced within the muscle that prevented the classic improved response to the

exercise stimulus. It has been postulated in a recent meta-analysis by Ekelund et al (39)

53

that individuals experiencing high levels of daily inactivity are at an increased risk of

mortality even when participating in similar levels of daily activity (e.g. MET hours).

Taken together it seems that reduced contractile activity causes a condition in which current

exercise recommendations may not be enough to derive protective benefits. Thus a higher

minimum level of recommended physical activity may be needed for populations regularly

experiencing prolonged inactivity.

Muscle lipoprotein lipase (LPL) is the rate limiting enzyme for clearance of plasma

triglycerides (202). Therefore, decreased LPL activity is a rational candidate for explaining

the increased PPL found in this study with LOW and LIM. Although not measured directly

in the present investigation, low levels of contractile activity have been observed to

drastically reduce the activity in LPL in muscle. In an animal model, hind-limb

immobilization has been shown to have sizeable reductions (i.e. 90% decrease) in LPL

activity and developed rapidly (>60% reduction in <12h) (15). However, it seems the

downregulation is post-transcriptional. Even in the tissue with more than 90% reduction in

LPL activity, LPL mRNA was similar to baseline (15). Additionally, the data from this

study (15) indicate LPL mRNA is not increased with walking or levels of contractile

activity associated with maximal increases in LPL activity. It seems LPL activity may be

down regulated by GPIHBP1 protein endocytosis induced by some co-factor produced

during periods of prolonged inactivity (12, 94). However, this hypothesis was not tested in

the present study and needs additional investigation to fully elucidate a mechanism.

It is plausible that this study would have a greater impact if it were conducted in an

older population. This is due to the increased magnitude and duration of the postprandial

triglyceride elevation in older populations, compared with their younger counterparts (11,

62). Postprandial TG concentrations have been observed to peak 2-3 hrs earlier in young

people while aged participants showed increased rates of chylomicron accumulation

54

peaking at concentrations nearly four-fold greater (141). The durations of these elevations

above baseline mirrored these findings, returning to baseline after 6 hours in the young

compared to 24 hours in an older population (11). However, it has been postulated that this

increased PPL is due to the decreases in non-exercise activity associated with aging (62).

Moreover, technological advances have drastically decreased occupational physical

activity such that activity in much of the workforce can approximate that of sedentary,

elderly individuals (26). This is not obviated completely even in health-conscious

individuals. For example, ‘workday’ sedentary time in 208 marathon and half-marathon

participants was observed to be similar to those seen in the elderly in assisted living

communities (90, 206). Thus, this work addresses a population that may still be at

significant health risk.

In conclusion, to the best of our knowledge the current investigation is the first to

indicate that 2 days of step reduction can decrease an individual’s responsiveness to an

acute aerobic exercise bout in terms of stimulating improved PPL and fat oxidation. When

participants took 8,480 daily steps and performed a 1h bout of exercise, their responses in

TG excursions and AUCs were significantly lower than following the same exercise with

step counts at 2,675 and 4,759 steps/day. When viewed from the perspective of previous

literature, the reduction in TG AUCI when participants took 8,480 steps, compared to either

of the lower daily step counts in this study, were similar to expected reductions that have

been observed to occur in a non-exercise control (46). This may support the exercise

resistance phenomenon recently coined by Kim et al (98), in which individuals who spend

the majority of their day sitting and take relatively few steps are unable to reap the benefits

generally associated with acute aerobic exercise (39, 124). Based on these data, from the

current investigation and others, it seems that reducing daily steps may cause development

of a condition in which the inactive muscle demonstrates blunted responses to normal,

55

healthy stimuli. Recent observations support this contention in that not only PPL is

impaired but reduced myofibrillar protein synthesis can be seen in elderly (22) and young

healthy individuals (166) in response to step reductions to ~1400 steps/day of two weeks

or less. These findings coupled with previous work on exercise resistance (1, 38, 98)

emphasize the necessity of maintaining a sufficient amount of physical activity (i.e.; >8,500

Steps/day) to ensure healthy PPL responses, even in participants exercising for 1h at 64%

VO2max.

56

Tables and Figures

Figure 7. Study Design. Participants completed a five-day randomized, crossover

experimental design with differing levels of daily step reduction (i.e. Low- 2,675,

Limited- 4,759, & Normal Activity-8,480 Steps/Day). Participants completed two control

days with activity monitoring before the initiation the two-day step reduction (D1 & D2).

Participants also completed an hour of treadmill running on the night of D2 followed by

HFTT on the morning of D3.

57

Figure 8. Total and Incremental areas under the curve of plasma triglyceride

concentrations during HFTT for each trial. (*) significantly different from Low &

Limited step group, p<0.05. (†) significantly different from Low step group, p<0.01. Data

reported as meanSE.

Low Limited Normal0

200

400

600

800

1000

1200

Tri

gly

ceri

de (

mg

/dL

*6h

)

Plasma Triglyceride Total Area Under the Curve

†

Low Limited Normal0

100

200

300

400

500

Tri

gly

ceri

de (

mg

/dL

*6h

)

Plasma Triglyceride Incremental Area Under the Curve

*

58

Figure 9. Total and Incremental areas under the curve of plasma glucose concentrations

during HFTT for each trial. Data reported as meanSE.

Low Limited Normal0

200

400

600

800

1000G

luco

se (

mg

/dL

*6h

)

Plasma Glucose Total Area Under the Curve

Low Limited Normal0

50

100

150

200

250

Glu

co

se (

mg

/dL

*6h

)

Plasma Glucose Incremental Area Under the Curve

59

Figure 10. Temporal Responses of plasma triglyceride concentrations for each trial

during HFTT. (*) Normal significantly different from Low, p<0.05. (†) Normal

significantly different from Limited, p<0.05. (#) Normal significantly different from

Low, p<0.01. Data reported as meanSE.

0 1 2 3 4 5 6

50

100

150

200

Time (Hours Postprandial)

Tri

gly

ceri

de (

mg

/dL

)

Plasma Triglyceride Response

Low

Limited

Normal

*

*† #†

60

Figure 11. Temporal Responses of plasma glucose concentrations for each trial during

HFTT. Data reported as meanSE.

0 1 2 3 4 5 6

80

100

120

140

Time (Hours Postprandial)

Glu

co

se (

mg

/dL

)Low

Limited

Normal

Plasma Glucose Response

61

Table 4. Descriptive statistics for participants at the beginning of the study. All data

reported as mean SE.

Physical Characteristics mean SE

Age (y) 23.4 5

Height (cm) 166.4 7.9

Body Mass (kg) 74.4 16.9

BMI (kg·m-2) 26.7 5.2

Note: Data are presented as MSE

62

Exercise Responses mean SE

Maximal Oxygen Consumption

VO2max Absolute (ml/min) 3,405 242

VO2max Relative (ml/kg/min) 42.7 1.4

Submaximal Exercise During 1-h Run

Heart Rate (bpm) 154 4

Rating of Perceived Exertion 11.4 1.1

VO2 (ml/min) 2210 154

% VO2max 64.4 0.4

Treadmill Speed (mph) 4.8 0.2

Note: Data are presented as MSE

Table 5. Responses to maximal exercise and the 1-h bout of submaximal exercise.

All data reported as mean SE.

63

Trial

Day of Trial

C1 C2 D1 D2

Daily Steps

Low 10198 1476 11015 1159 2744 331 2605 313

Limited 9808 1207 10568 1112 4482 318* 5037 206**

Normal 11056 1324 10732 936 8431 732† 8530 420†

Note: Data are presented as MSE. (*) Significantly different from Low, p<0.05. (**) Significantly different from Low, p<0.01.

(†) Significantly different from both Low & Limited, p<0.01

Table 6. Average daily steps measured via activPal activity monitor, attached on the

participant’s anterior thigh throughout each trial. Average daily step counts for each trial

are presented for Control (C1 & C2) and Intervention Phases (D1 & D2). (*) significantly

different from Low, p<0.05. (**) significantly different from Low, p<0.01. (†)

significantly different from Low & Limited step trial, p<0.05.

64

Trial Postprandial Time (Hours)

Baseline H2 H3 H4 H6

Triglyceride Concentration (mg/dl)

Low 89.8 7.5 147.5 13.5 172.4 13.0 176.5 16.2 133.0 13.6

Limited 81.1 9.1* 134.0 11.1 175.4 15.5 167.6 16.3 126.2 14.2

Normal 80.0 5.8 125.3 9.8* 151.3 11.3*† 141.6 11.9*† 115.7 12.2

Glucose Concentration (mg/dl)

Low 91.8 4.3 116.4 6.7 --- 139.7 16.8 116.3 10.4

Limited 92.5 3.4 115.4 5.7 --- 122.7 10.6 110.1 5.4

Normal 94.5 3.7 108.2 4.4 --- 130.4 7.3 111.4 6.2

Table 7. Hourly responses (e.g.; H2, H3, etc.) of plasma triglyceride and plasma glucose

concentrations during HFTT for each trial. (*) Significantly different from Low, p<0.05.

(†) Significantly different from Limited, p<0.05. Data reported as meanSE.

65

Variables Treatment Group

Low Limited Normal

RER 0.81 0.01 0.80 0.01 0.77 0.01*

Fat Oxidation (%) 66.1 4.87 69.6 3.93 80.4 2.65*

Fat Oxidation (kcal/6h) 318.9 34.5 342.4 30.9 396.0 27.5*

Carbohydrate Oxidation (%) 33.9 4.87 30.4 3.93 19.6 2.65*

Carbohydrate Oxidation (kcal/6h) 164.0 25.3 149.0 24.1 97.8 12.8*

Total Energy Expenditure (kcal/6h) 482.9 32.6 491.4 31.2 493.8 27.0

Table 8. Overall postprandial substrate oxidation during HFTT for each trial. (*)

significantly different from Low & Limited, p<0.05. Data reported as meanSE.

66

Chapter VI: General Summary

These studies were conducted in order to determine: 1) if the background level of

daily physical inactivity impairs postprandial lipemia (PPL) and cardiovascular adaptations

to short term training and 2) the effect of altering daily step counts for two days on the

ability of a 1-h bout of moderate-intensity exercise to reduce PPL.

In Study 1, it was shown that inducing physical inactivity by reducing daily step

count to 4,767 steps over 11 days, in conjunction with vigorous-intensity exercise training,

resulted in an inability to incur the classic PPL-lowering effect of acute exercise as well as

short term training. Furthermore, classic adaptations, such as decreases in heart rate and

blood lactate concentration and increases in muscle oxygenation and fat oxidation when

exercising at an absolute workrate, typically seen with exercise training and displayed in

the High Step group (p<0.05), were not significant in the Low Step group. This is consistent

with the findings of Kim et al. (98) that first coined the term ‘exercise resistance’, but also

extends this phenomenon to an impairment of short-term training adaptations as well as

measures of postprandial metabolism. These data indicate that, even in individuals who

participate in the early stages of regular exercise training, background inactivity results in

elevated PPL and impaired cardiometabolic adaptations to short term training.

In Study 2, it was demonstrated that reducing daily steps to 4,759 or below is

associated with a decreased ability of acute exercise to lower PPL, compared to a trial

taking 8,480 steps/day. Responses to a high fat meal on the morning following a 1-h run at

64% of VO2max were similar in groups taking 2,675 (LOW) and 4,759 steps/day (LIM).

However, following an identical exercise bout, participants taking 8,480 steps/day

(Normal) significantly reduced their plasma triglyceride incremental area under the curve

compared to LOW and LIM trials (p<0.05). This indicates that the development of the

67

aforementioned ‘exercise resistance’ occurs when steps/day drop below approximately

5,000. These findings suggest that some beneficial effects of acute exercise can be

diminished if individuals experience regular periods of inactivity leading to reduced daily

steps (i.e. below approximately 5,000 steps/day).

Taken together, these studies suggest deleterious effects of inactivity on an

individual’s ability to properly respond in terms of cardiometabolic adaptations (i.e; PPL,

HR, lactic acid, fat oxidation and muscle oxygenation) to an exercise stimulus provided by

1-h of running or 9 days of chronic training. The Low Step treatment group in study 1 took

approximately 5,000 steps/day which is similar to the limited step trial (LIM) in study 2.

This is useful in comparing the effects of reduced step counts in both studies. Taking

~5,000 steps/day seems to have rendered the exercise training ineffective in lowering PPL

in study 1 in that postprandial responses following training were not significantly different

from baseline responses. Additionally, data from study 2 shows acute exercise while taking

~5,000 steps/day or less (i.e. LIM & LOW trials) is less effective at lowering PPL

compared to taking ~8,400 steps/day. Jointly the findings in this dissertation suggest that

< 5,000 steps/day is insufficient to realize the protective effects of acute or short term

exercise training, at least in terms of PPL and fat oxidation (Table 9). The exercise

resistance phenomenon first postulated by Kim (1) seems to manifest itself when taking

less than 5,000 steps/day, regardless if the exercise is of moderate or vigorous intensity and

persists even with regular, short-term training. It should be realized however, that although

taking less than 5,000 steps/day appear ineffective in preventing exercise resistance, and

taking approximately 8,500 steps/day is effective, it is still unclear how activity levels

between these step counts affects the development of this phenomenon.

These findings suggest that reductions in daily step counts are associated with

impaired cardio-metabolic responses (i.e.; postprandial lipemia and fat oxidation) to both

68

acute and short term exercise training. These studies have some noteworthy strengths in

that they add pertinent information to the literature especially surrounding the newfound

“exercise resistance” phenomenon. To the authors knowledge this is the first study to

consider the interplay of physical inactivity and short-term training. Moreover, the second

study provides important information on hoe to characterize the onset of this new

phenomenon. Lastly, these findings suggest that even young, healthy individuals are

susceptible to negative cardio-metabolic effects of physical inactivity that cannot be easily

overcome with exercise.

Trial

Study 1 (Training) HIGH STEP LOW STEP

Steps per Day 16,048 4,767*

Study 2 (Acute) NORMAL LIMITED LOW

Steps per Day 8,431 4,759 * 2,744*

Table 9. Summary of findings (*) signifies impaired metabolism

69

Chapter VII: Review of Literature

Introduction

The perils of physical inactivity have been generally appreciated for several

millennia. Susruta, an Indian physician in the 7th century BC, may have been the first

physician to prescribe daily activity. He believed daily physical activity was necessary to

ward-off disease, even stating “diseases fly from the presence” of such individuals

participating in habitual physical activity (177). Not long after in the 5th century BC,

Hippocrates is quoted as saying “walking is man’s best medicine” and, “all parts of the

body… if they are unused and left idle, become liable to disease…” (106). Moreover, the

effects of physical inactivity have been observed and studied systematically since the

1950’s. The seminal work by Morris et al. (133) found an increased rate of coronary

artery disease in London bus drivers compared with their more active counterparts on the

busses, the conductors. These findings suggest that individuals who are chronically

inactive can suffer serious detriments to health as a result. Since then, numerous studies

have been conducted to evaluate the deleterious effects of physical inactivity. However,

even with the recent interest and the emergence of the field of inactivity physiology (63),

it seems we have only scratched the surface in understanding the harmful effects of

inactivity and its increasing prevalence in many modern cultures.

In modern culture we are continually engineering activity out of our daily lives.

As such, periods of prolonged inactivity have become routine in the lives of many. This

is especially prevalent in well-developed countries in which technological advances allow

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for increased automation and vehicular transportation (217). The populations of such

societies also rarely go more than 8 hours without eating. Consequently, these individuals

spend most of their time in a non-fasting or postprandial state. As a consequence of an

increasingly sedentary lifestyle, periods of prolonged sitting and the postprandial state

routinely coincide resulting in chronically elevated levels of plasma triglyceride and

glucose. The consequences of prolonged sitting have begun to be recognized by some

countries who have already made a conscious effort to advise against prolonged bouts of

sitting (217). In the postprandial state, triglyceride levels in the plasma can remain

elevated for up to 10 hours, typically peaking 3 - 6 hours after a meal rich in fat (152).

The magnitude and duration of this elevation is influenced by prior physical activity (52,

120, 221), diet (174), and genetics (173, 204). As demonstrated in recent epidemiological

studies (5, 142), non-fasting plasma triglyceride levels, i.e., post-prandial lipemia (PPL),

better predicts cardiovascular events than fasting plasma triglyceride levels and are

known to be associated with diseases, including metabolic syndrome, type 2 diabetes, and

atherosclerosis.

In several other recent epidemiological studies, sitting time has been strongly

linked with the risk of obesity, metabolic disorders including type 2 diabetes mellitus and

especially with cardiovascular disease and death (16, 198). Exercise as an intervention

has been studied with promising results (76, 120, 222). However, recent epidemiological

studies have reported that the risks from prolonged sitting appears “independent” of the

volume of exercise being performed (16, 150, 198). This means individuals who meet the

recommended guidelines (AHA or ACSM) for physical activity of 150 min/week of

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moderate intensity exercise appear to still be at risk for developing cardiovascular disease

if they have a lifestyle routinely incorporating prolonged periods of sitting (>10-12 h/day)

(39). In 2016, the term ‘exercise resistance’ was first introduced to describe a

phenomenon in which individuals who experience prolonged periods of inactivity seem

unable to realize some of classic metabolic benefits associated with an acute bout of

aerobic exercise (98).

Postprandial Metabolism and Health

In order to best analyze the effects of physical inactivity on health, it is necessary

to establish a measure that can serve as a proxy for cardiometabolic health and is

sensitive to the changes associated with inactivity. The method that seems to be the most

promising is the measurement of blood lipids, in both the fasted and non-fasted state.

Much is understood about the dangers linked to elevated fasting plasma triglyceride

levels (79, 121). However, recent epidemiological and scientific evidence suggests

postprandial lipemia (PPL) is a stronger indicator of CVD risk than is fasting plasma

triglyceride level (5, 87, 88, 142).

Chronic dyslipidemia and the oft-resultant atherosclerosis are two of the principal

contributors to CVD (87, 207, 223, 224). Within first-world countries, where food is

plentiful, a significant amount of time is spent in the postprandial state, leading to longer

periods of elevated triglycerides. These increased PPL levels are associated with reduced

high-density lipoprotein production and increased low-density lipoprotein cholesterol

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production (44), impaired endothelial function (199), and increased atherosclerotic

plaque formation (223).

There are two major sources of circulating triglycerides. These can be produced

from an endogenous pathway within the liver (e.g. lipogenesis) or can be consumed

through exogenous dietary sources (73). Dietary consumption can lead to two different

ultimate destinations of exogenous lipids, depending upon their type. Short-to-medium

chain fatty acids are transported primarily to the liver, then successively to their final

destination, often being skeletal muscle, undergoing beta oxidation to aid in successfully

supplying the energy needs of the tissue. However, triglycerides composed of long chain

fatty acids, are carried predominantly via chylomicrons and/or very low-density

lipoproteins (VLDL), and transported to adipose and muscle tissues (144). The main

transporter of endogenous lipid production are VLDLs. Conversely, for ingested

triglycerides, chylomicrons are the primary vehicle (56, 140). Chylomicrons are formed

in the endoplasmic reticulum of small intestine enterocytes and are secreted into the

lymphatic system. Subsequently chylomicrons travel through the lyphatic system and

enter systemic circulation via vena cava (86). In the postprandial state, the formation of

chylomicrons may compete with lipoproteins to interact with lipoprotein lipase (LPL), an

enzyme located on the luminal side of vascular endothelial cells in adipose, skeletal

muscle, and myocardial tissue (18, 32, 201). LPL hydrolyzes the triglycerides from both

VLDL and chylomicron sources. However, after a meal as the chylomicrons

concentration increases, VLDL production continues as it is modified by liver

concentrations of FFAs. The result is a level of circulating triglycerides and cholesterols

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that is elevated significantly (223). As chylomicrons and VLDLs both utilize LPL as an

uptake mechanism, postprandial saturation of LPL can occur as the two molecules

compete for binding sites (32, 201). Therefore, this increase in circulating

cholesterol/triglycerides and concomitant saturation of LPL allows for the opportunity for

chylomicron and VLDL byproducts to build up in the subendothelial space. During

instances where dietary consumption leads to an increase in PPL. Furthermore, residual

fatty acids from chylomicron hydrolysis may be re-esterified in the liver as VLDL and

eventually used to synthesize various byproducts, including low-density lipoproteins

(LDL) (55, 87). It is this production and accumulation of LDL, and other VLDL

remnants which is the genesis of atherosclerosis (59, 223, 224).

Atherosclerosis has numerous sources that contribute to its development including

endothelial dysfunction (3, 33) and abnormal blood lipids (134, 142, 223). The most

common explanation for the onset of atherosclerotic formation begins with an increased

accumulation of lipoprotein behind the endothelial wall of the vasculature (117, 138,

209). Here these lipoproteins undergo modification through oxidation, lipolysis,

proteolysis, and aggregation. Eventually fostering the formation of foam cells via

macrophage infiltration/conversion and to inflammation of the surrounding tissue (117,

172). The resulting damage is eventually repaired but leaves behind underlying tissue

which may begin to form a necrotic core and exterior calcification. This tissue is then

vulnerable to later injury which may cause release of the interior contents of the lesion

and can lead to thrombosis (117, 171). This thrombosis is a likely cause of cardiovascular

events such as stroke or myocardial infarction.

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Prevalence of Inactivity in Modern Culture

The magnitude and consequences of physical inactivity in modern times have led

to it being appropriately termed a global pandemic (105). In order to understand the

prevalence of inactivity in a modern lifestyle, it is important first to define the terms by

which one would be considered inactive. In 1995 the Centers for Disease Control and

Prevention (CDC) and American College of Sports Medicine (ACSM) published

minimum recommendations prescribing 30 minutes of moderate intensity exercise on

most, preferably all, days of the week (149). These guidelines were recently updated with

additional support for beneficial effects of physical activity and the removal of a 10-

minute minimum duration for activity (157). This level of physical activity equates to

about two miles of brisk walking, accumulated throughout the waking hours of an

individual’s day. Physical inactivity is generally defined as failing to meet these

requirements. Specifically, failure to achieve 150 minutes of weekly moderate to

vigorous physical activity (MVPA), 75 min of vigorous physical activity, or a combined

equivalent achieving 600 metabolic equivalent (MET)-minutes per week (208, 213, 214).

Based on accelerometer data from the National Health and Nutrition Examination

Survey, the prevalence of meeting these guidelines could be as low as 5% in the United

States (182). Other more hopeful, yet still concerning estimates put this number at about

31% meeting guidelines (60).

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It is necessary to understand why this number is so low. Much of the increase in

inactivity can be linked to modernization of the culture and the technology associated

with said modernization. As societies continue to advance, technology must improve in

order that the time spent in menial tasks can be better invested in more meaningful

pursuits which spur further progress. However, it seems rather than current technology

providing time savings that can be reinvested in other activities, it is instead being

replaced by inactive behaviors causing a dramatic rise in daily inactivity.

For example, estimates of daily step numbers indicate the introduction of powered

machinery has causes a decrease of 50-70% of daily activity (19, 20, 143). Even in

modern times populations, such as some Amish communities, who limit or completely

abstain from the use of many modern technological conveniences take four times more

daily steps as those who do not (9, 10). This is bolstered by observations like the 2017

American Time Use Survey which revealed just over 5% of leisure time is spent in

“exercise, sports, and recreation” (196). It seems that the lives of many are dominated by

sedentary actives such as prolonged sitting. Some have suggested it is possible, if not

likely, that 95-97% of an individual’s waking hours could be spent in sedentary activities

(72, 205). Sedentary activities are defined as activities that involve energy expenditure at

the level of 1.0-1.5 metabolic equivalent units (METs)(148). More tangibly, sedentary

behavior includes activities that do not increase energy expenditure markedly above the

resting level. Such as sleeping, sitting, lying down, and watching television. While

exercise is prescribed broadly, a 30-minute daily session still allows for upwards of 16

hours of inactivity. Further, evidence suggests that there may be no difference in sitting

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time between those who achieve recommended levels of physical activity and those who

do not (30).

Engagement in light-intensity activity may be the best way to reduce sedentary

behaviors. One of the strongest negative correlations (r=-0.98)(71) in relation to

sedentary time is time in light physical activity, such as walking, which involves energy

expenditure at the level of 1.6-2.9 METs. (65, 139, 148). While the energy expenditure

may only be slightly above that of sitting, these activities are the predominant

determinant of overall daily energy expenditure - even in those who exercise regularly

(34). Unfortunately, modern cultures fall short on this score as well. Tudor-Locke et al.

(194) provides data suggesting 17% of US adults take fewer than 2,500 steps per day.

Further about 37% take fewer than 5,000 steps, with anything under this level being

considered by most to be indicative of a very inactive or sedentary lifestyle (163, 191,

194). A decrease of 2,500 steps/day is associated with increases in sitting time on the

order of 37-45 mins/day (191) or as much as 75 minutes (128).

Another area that has seen a dramatic rise in inactivity is occupational time. (26,

101). Nearly half of occupations in the 1960s could be characterized as requiring

moderate activity. That number had dropped to less than 20% by 2008 and this trend is

expected to continue (26). These authors concluded that the decrease from occupational

energy expenditure over 4 decades could almost entirely explain the increase in weight

seen in the NHANES study over the same period (26). This trend effects even those who

are most active. Data from one study (206), more clearly illustrates this alarming point. In

this study, marathon and half marathon participants who, by the nature of their

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recreational choices, are presumably very active were found to have daily sitting times

roughly equivalent to those found in elderly community-dwelling populations during the

work week (90, 206).

Deleterious Effects of Inactivity

The prevalence of inactivity is extremely concerning, especially given the effects

on the health of millions. While the health hazards do not command the same attention

from governments and health organizations, the deleterious effects of inactivity are

similar to, if not greater than, those seen with smoking and obesity (112). The impact of

these on an individual health can be more hazardous but, the prevalence of inactivity is

much higher than either smoking or obesity, leading to an effect on the population that is

no doubt more severe. In 2009, physical inactivity was officially recognized as the fourth

independent risk factor for non-communicable diseases and accounted for more than 5

million preventable deaths per year (112, 213).

It is estimated that 20% of all CVD and 10% of strokes occur due to physical

inactivity (35) and over 30% of ischemic heart diseases (105). According to data

published in The Lancet, inactivity is a causal factor in 9% of premature mortality (112).

Further, a reduction in the prevalence of inactivity by just 25% worldwide would prevent

over 1.3 million deaths each year (112). Well-reasoned estimates contend that if every

person in the United States were to meet physical activity guidelines nationwide, life

expectancy would increase by at least 0.68 years on global life expectancy (112). This

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number may seem low, as discussed by the authors, but when viewed in the proper light it

is actually substantial. That is, those who are already sufficiently active would see no

increase in life expectancy, therefore inactive individuals under this estimate could see an

increase of four or more years in life expectancy.

One of the starkest examples illustrating the potency of inactivity to cause

deleterious health outcomes is the classic Dallas Bed Rest Study. In this study, trained

participants underwent 20 days of bed rest. This intervention induced reductions of over

25% in VO2max, cardiac output, and stroke volume and an 11% decline in total heart

volume (162). In a follow-up study, McGuire et al. found that these decrements were

greater than the decline in these variable resulting from 30 years of aging (125). Given

the inverse relationship between cardiorespiratory fitness and all-cause mortality (103) it

is clear that inactivity, at least with respect to this level of inactivity, is particularly

unhealthy.

It is important to understand why these concerns are especially pertinent to first

world countries like the US in which heart disease is the number one cause of death

(102). CVD and premature mortality seem to be strongly linked and show a direct dose

response relationship to physical inactivity (39, 104). Therefore, several other models of

inactivity have been employed in studying the deleterious effects that lead to these health

outcomes. Dunstan et al (2004) observed that, when comparing TV time, those who

spent the most time watching television were more likely to have diabetes. Additionally,

when comparing those with the most TV time to group with the least reported time,

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women were more likely to have compromised glucose tolerance. These results held even

after adjusting for total time in physical activity as well as other covariates (37). Another

analysis of prospective data (84), found a significant trend for increasing obesity and

Type II diabetes risk across categories of increasing TV viewing time. Again, this trend

remained significant after adjusting for covariates including a measure of exercise

participation. Katzmarzyk, et al. (93) provided data from a prospective study of physical

activity, sitting time and mortality. These data revealed a significant dose-response

relationship between sitting time and both all-cause and cardiovascular disease mortality.

Intriguingly, When participants were stratified by activity this dose-response relationship

was slightly attenuated, but persisted even among those meeting PA guidelines (93)

Due to these and other similar revelations, a new field of inactivity physiology was born.

The main contention of this field is that inactivity is harmful in that it not only impacts

typical exercise induced health benefits but prompt a set of completely divergent health

implications that arise not from the lack of exercise but from the effects of inactivity (64,

147).

Exercise and Postprandial Metabolism

Physical inactivity has been recognized as a major independent risk factor in the

development of CVD (28). Thus, it stands to reason that one remedy for the rise of CVD

in modern culture would be engaging in regular exercise. Exercise is universally

recognized to decrease the risk of a variety of chronic diseases and metabolic disorders

80

(154). Exercise training has been shown to reduce risk of atherosclerotic plaque

formation associated with excessive blood lipid concentrations (43),which leads to lower

cardiovascular disease (CVD) risk in individuals who are physically active compared to

inactive individuals (14). More specifically, a single bout of exercise can have impact on

PPL by affecting circulating triglyceride levels following a meal (46, 76, 120, 168, 222).

Studies using an intravenous lipid tolerance test indicate that the protective effect of an

intense bout of exercise is attributed to accelerated TG clearance from the circulation

(165)

This effect of exercise is fairly robust. Prior exercise attenuates PPL when a meal

is given several hours after exercise (i.e.12 h) regardless of whether the meal is of

moderate or high fat (85), or exercise is of low, moderate, or high intensity (129, 183,

186) and even in response to resistance exercise (186, 219). There are several variables

that can influence the exerted effects of exercise such as timing of exercise, intensity, and

energy balance as well as the composition of the test meal.

Timing of the exercise seems to be an important factor in assessing this effect. It

is important to understand when a bout of exercise begins to exert a sizable effect on

postprandial metabolism and when that effect subsides. Once these are correctly

understood, it becomes easier to sustain healthy plasma triglyceride levels and ward off

atherosclerosis and associated CVD.

It seems that the delayed effect (>12h) of exercise on PPL is more robust than the

acute effect (222). When test meals are given immediately following exercise or closely

there after (<4h) some mixed results have been reported (124). During this time many

81

factors can confound the effect of exercise such as the flux in fasting triglyceride levels

from the resultant increase in lipolysis and subsequent increase in free fatty acid (FFA)

delivery to the liver (175). Some of these studies show discernable difference in PPL in

response to a high-fat meal, but these differences could not be seen if a moderately-fat

test meal was employed (155, 158). The lack of a difference persists in cases where either

moderate or low intensity exercise was undertaken (155, 156). Also during this time,

very-low intensity (25-30% VO2max) and resistance exercise have shown no effect, or

even adverse effects on PPL (23, 92).

Conversely, when a test meal is given 12-16 hours after an exercise bout the

effects are much more ubiquitous on PPL. The effectiveness of exercise is apparent over

a vast range of intensities from 25-90% of maximal oxygen uptake (VO2max), and

durations of exercise ranging 30-120 minutes (52, 76, 120, 130, 168, 183, 222). This is

also true regarding the mode and type of exercise. As previously mentioned, aerobic and

resistance training both show clearly discernable benefits on PPL. High intensity interval

training (HIIT) has also been employed and seems to show an even greater ability to

attenuate PPL (47, 183)

However, this attenuation does not continue indefinitely. Data suggest this effect

of acute exercise is transient in nature and may only continue to show appreciable effects

on PPL for 24-40 hours (76, 124, 187). It seems that while this attenuated PPL response

is still distinct from non-exercising controls up to 42 hours post exercise, (75, 76) but this

difference is no longer apparent after 60 hours (76, 77). Therefore despite evidence that

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active middle-aged men have lower plasma triglyceride concentrations than their

sedentary counterparts, both fasting (83) and in response to a high-fat meal (164), this

training provides no protective effect after the transient effects of the last bout subsides in

42-60 h. This has been seen in investigations ranging from 1 week of training to 6

months. After one week of training investigators report improved postprandial

metabolism in the absence of any other metabolic changes, suggesting the improvements

are a transient and induced by activity in the final bout of exercise (159). When

participants are asked to abstain from exercise for 60 h prior to measuring PPL, the

difference between active (endurance and sprint/strength trained) and inactive

counterparts is abolished (185). Interventional studies have shown that one month of

aerobic training appears ineffective in positively influencing PPL, when it is measured 2

(53, 169), 2.5 (77), or 9 days (7) after detraining.

Intensity is also an important factor in modulating this PPL response. A bout of

high intensity interval aerobic exercise or resistance exercise may reduce fasting plasma

triglycerides the next day in a similar magnitude and via a similar mechanism as

moderate intensity aerobic exercise of almost twice the energy expenditure (24, 67).

Furthermore, HIIT training has been shown to induce a larger reduction in the

incremental area under the curve (iAUC) response than aerobic (46, 184). Freese et al.

(47) found that an accumulation of 18 minutes of HIIT could induce an attenuation of

PPL similar to continuous aerobic exercise lasting 30 minutes or longer. Further these

authors compared 30 minutes of brisk walking to five, 30 second maximal sprints with 4

minutes of rest and found that only the HIIT sprints caused a reduction in PPL

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incremental AUC (48). Some contend that this method is flawed in the inability to

accurately account for total energy expenditure because of the anaerobic component of

this type of exercise and subsequent increase post-exercise oxygen consumption (46).

However, when Trombold et al. (183) compared 2 min intervals at 90% of VO2peak to 1

hour of continuous exercise at 50% of VO2peak resulting in the same energy expenditure,

these investigators found the intervals significantly reduced PPL compared to control and

continuous exercise. The results indicated HIIT exercise was better at attenuating PPL, as

the investigators found a significantly reduced triglyceride incremental AUC when

compared to the continuous exercise and non-exercise groups (183). Nevertheless, the

same group produced data indicating that intensities as low as 25% VO2max still

produced significant differences in PPL compared with controls (99). Cumulatively these

data suggest that while a large range of intensities can be employed to combat PPL,

increased intensities do, in fact, result in more effective and potent reductions.

It has been suggested that this most crucial variable in permitting an attenuation

of PPL is not the intensity or duration of exercise but the existence of an energy deficit

resulting from the exercise undertaken (8, 46, 124, 129, 178). Under this premise, Gill

and Hardman (52) investigated energy deficits induced by exercise and reduced caloric

intake. They found caloric restriction produced positive results but the deficit imposed

via exercise was superior at reducing PPL compared with equivalent deficits resulting

from caloric restriction. However, aerobic exercise has been shown to lower plasma

triglyceride concentrations in the absence of concomitant energy deficit (2). Tolfrey et al.

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(179) found that high intensity cycling can reduce PPL, even when energy expenditure is

replaced. However, a still greater effect existed in a group which remained hypocaloric,

suggesting an interaction.

One prevailing hypothesis suggests that the mechanism by which these exercise-

induced improvements in postprandial metabolism are achieved is via an increase in

lipoprotein lipase (LPL) activity within the active skeletal muscle (46, 124). This is

supported by studies suggesting the exercise-induced attenuation of PPL cannot be

attributed to acute exercise-induced changes in blood flow or energy stores (225).

Skeletal muscle LPL, found on the vascular endothelium, is the main site of triglyceride

removal, and the activity of LPL can be increased through exercise (58, 164, 165). It

seems contractions of the skeletal muscle cause a transient (165), tissue specific (164)

increase in skeletal muscle LPL enzyme activity. This increase in LPL activity, as well as

protein, is a local, delayed response to contractile activity and is independent of

catecholamine and other cardiometabolic responses to exercise (58). The exercise-

induced increase in LPL mRNA levels peaks 4 h after exercise, whereas LPL protein

peaks 8 h after exercise and returns to baseline values within 24 h post-exercise (97, 165).

While exercise has a robust effect on LPL, inactivity may have an even greater effect.

Over 90% of LPL activity typically present in skeletal muscle can be lost by preventing

ambulatory activity, while light activity has been shown to increase LPL activity (15). In

fact, Bey and Hamilton (15) reported that intensities roughly equivalent to casual walking

could maximally activate LPL in slow muscle fibers.

It seems that elevated plasma insulin concentrations can suppress LPL activity

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(97). Three days of high carbohydrate (CHO) diet results in increased plasma insulin and

55% lower LPL activity (89). Conversely, a low-CHO diet for the same duration results

in an increase in LPL activity (115). This may serve to reconcile some of the spurious

results found in previous research. Even if the same exercise was employed the

carbohydrate content of the test meal could results in divergent responses. To address

this, Trombold et al (184) used a high (83%) and low CHO (12.5%) test meal following

identical exercise regimens. In this study, although both replaced the exercise-induced

calorie deficit, only the group given the low-CHO test meal showed reductions in PPL. It

stands to reason that an exaggerated rise in plasma insulin could cause a subsequent

suppression of LPL activity in response to the high carbohydrate content of the test meal.

Nevertheless, exercise-induced reductions in PPL have also been documented even in the

absence of significant changes in plasma insulin or improvements in insulin sensitivity

(54, 123, 129).

Alterations in Ambulatory Activity

It is well established in the current literature that regular physical activity,

including exercise, is advantageous for those seeking to reduce risk of detrimental health

outcomes (68). Exercise is different from physical activity by its purposive nature (25).

While the dramatic effects of the various types and modes of exercise have dominated the

literature, more recently the effects of changing regular non-exercise physical activity

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have gained a greater appreciation and considered more frequently in interventional

studies.

Light intensity activity has an inverse linear relationship with a number of cardio-

metabolic markers and the impact of these activities as a biological stimulus contributing

to better health has probably been significantly underestimated (36, 72). Much of this is

attributed to the replacement of unhealthy behaviors with healthy ones. In fact, almost all

variation in sedentary time across the population is related to the extent to which

sedentary time is replaced by light intensity activity (36)

One of the most attractive and practical interventions currently used in

investigations of the effects of physical inactivity is reductions in daily step number. This

is because reductions in daily steps are less extreme than bedrest, spaceflight, and other

models of inactivity. Moreover, using easily accessible pedometers and accelerometers

makes monitoring much less arduous and more replicable. Due to the tangible nature of a

daily step number metric and the ability to employ these interventions under free-living

conditions, the conclusions garnered from these studies are more readily applicable to a

general population. This is also reasonable in view of growing epidemiological evidence

that suggest increased daily walking, which is the largest component of daily physical

activity (113, 114), is associated with decreased risk of cardiovascular events (122).

Conversely, decreased walking has been shown to have a number of adverse health

effects such as insulin resistance (109, 145) and obesity (114).

Several recommendations have been given as to the number of steps individuals

should accrue each day. Many cite a 10,000 step daily goal as a benchmark to improve

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health, although this seems to be a rather arbitrary number that finds its origins nearly 60

years ago in Japanese health clubs and pedometer promotions (189). Further studies on

this topic have focused on empirically-driven data to craft a daily step goal (119, 188).

Tudor-Locke et al (195) and others (161) have found that, when translating the current

PA guidelines into a standard daily step number, approximately 8,000 steps/day was

consistent with obtaining the recommended 30 minutes/day of MVPA.

While these studies are considered less extreme than studies of extended bed-rest

and the like, they are still quite potent with relatively short interventions exerting sizable

effects. Some authors have found reducing daily step numbers from >10,000 to <2,000

steps/day in participants that VO2max decreased ~7% in just two weeks (108, 109). These

data are underscored by the assertion made by Trappe et al (180) that the decline in

cardiorespiratory fitness from ages 30 to 50 is due almost exclusively to the increase in

physical inactivity. Further, as individuals age daily step counts decrease (190, 192, 215)

even as daily walking makes up a greater portion of an individual’s total physical activity

(182, 193).

Furthermore, reduced ambulatory activity in durations shorter than a week have

been shown to impair insulin sensitivity and elevated glucose responses to oral glucose

tolerance tests (OGTT) (108, 109, 128, 145). By experimentally reducing daily step

number for 1 week from ~10,500 to ~1,500 Olsen et al. (145) reported an increase of

more than 52% in the area under the curve of plasma insulin during an (OGTT) with the

potential to grow to nearly 80% greater, if the reductions were maintained for 2 more

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weeks (145). This is due to a decrease in insulin sensitivity and increases in insulin and c-

peptide in response to an OGTT within 3 days of reduced ambulatory activity. Due to

these changes, development of type 2 diabetes and metabolic syndrome become more

prevalent in inactive individuals. In an analysis of 2,500 participants Vander Berg et al

(197) found that for each additional hour of sitting or lying during the waking hours odds

of developing type 2 diabetes increased 22% and 39% for development of metabolic

syndrome. Interestingly these elevated odds were independent of participation in high-

intensity physical activity. Much less work has been done to directly investigate the effect

of decreasing daily steps and PPL responses. However, a two-week reduction in steps has

been associated with an increase of 27% in postprandial plasma triglyceride AUC (109,

145). This indicates the effects may be similar to those on glucose metabolism.

Exercise Resistance

Recently an alarming phenomenon of ‘exercise resistance’ has been postulated by

some in response to inactivity (1, 22, 38, 98). Relatively few studies have considered the

effects of inactivity on metabolism in conjunction with acute and chronic exercise. This

type of design is pertinent in a culture where individuals are able to achieve physical

activity guidelines (i.e. a 30-minute brisk walk) and sit for 15 hours or more in the same

day (64, 65). Epidemiologists have begun to recognize an alarming trend, identifying a

subset of those classified as “physically active” are not fully realizing the protective

effect of that activity (146). It seems that physical inactivity may cause the production of

some unknown factors that impair normally healthy physiological stimuli, such as

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exercise, from occurring or being realized. Or, alternatively, the adverse effects of a

physically inactive lifestyle may be independent of the protective effects normally

associated with exercise (36, 65, 146).

A plausible explanation to this trend arose in 2016 when the term exercise

resistance was coined in a recent paper from Kim et al. (98). In this study it was found

that participants that sat for ~14 hours (<1700 steps) in their waking day did not respond,

by attenuating the 6-hour PPL excursion, the morning after 1-hour of running at 63% of

VO2max,. Yet, participants in a group that were not sitting to such an extent did improve

PPL the morning after the acute exercise. Interestingly, this non-response was observed

in groups of both eucaloric and hypercaloric energy balance (98). While this was a

remarkable observation, the study was not designed to make definitive statements on

‘exercise resistance’, as it did not include a non-exercise control group. In order to

address this, a follow up study by Akins et al. (1) was conducted to test the existence of

this phenomenon. Using a similar, randomized, cross-over design, this study employed

inactivity to slightly lesser extent (~13.5h/day sitting and ~3600 steps/day) but provided

an adequate control in which one of the two trials entailed prolonged sitting without

exercise on the day before the HFTT. Following the 1h of exercise, the participants in

this study showed no significant improvement in PPL, glucose or insulin excursions over

6 hours compared to the non-exercise control group (1). Thus, they appeared resistant to

improving PPL as a results of the 1h of exercise.

Cumulatively, these finding indicate that chronic inactivity abolishes the

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beneficial effect of acute exercise on reducing PPL and bolstering fat oxidation. These

data show that, in participants who experience prolonged periods of inactivity, an acute

bout of exercise (e.g.; 1h of running) did not improve PPL. Due to this inactivity the body

appears to be resistant to deriving one of the main acute health benefits of exercise; in

this case attenuation of PPL.

In yet another study, Duvivier et al. (38) asked participants to undergo one of

three free living conditions. The participants either sat for 14 hours/day, sat for 13

hours/day with 1-hour exercise bout interjected to replace an hour of sitting, and a

condition with light physical activity that consisted of substituting 6 hours/day of sitting,

with 4 hours of walking, and 2 hours of idly standing. While both light PA and exercise

increased energy expenditure above sitting, neither differed from each other in total

energy expenditure even though the number of steps in the low-PA group was 5 to 6

times higher (38). Interestingly, the 1-hour bout of exercise was not able to improve

resting, fasting plasma triglycerides, cholesterol, or insulin concentration over sitting

alone. Conversely, minimal-PA was able to improve resting, fasting plasma triglycerides

and cholesterol over sitting and insulin concentration compared to the exercise group

(38). Although these results did not administer a HFTT and only reported no significant

effect on fasting levels the morning after exercise, it agrees with but does not prove the

concept of exercise resistance.

Naturally, questions arise as to which is the likely culprit inducing this

phenomenon of ‘exercise resistance’. Could it be there is something inherently harmful

with the seated posture itself or, rather, is it due to a lack of contractile activity within the

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muscle due to sitting? To answer this several studies have broken up prolonged periods of

sitting with standing and found no discernable reductions of triglyceride iAUC (4, 74).

Still, some doubted that the extent of standing employed in previous studies was

sufficient to induce a significant difference. Crawford et al (31) had participants stand for

12 hours, or more, on the day prior a HFTT. In this study, the standing intervention

group, displayed plasma triglyceride and insulin iAUCs that were no different than a

group who sat for more than 14 hours (31). In another study (211), participants completed

40-sprint bouts of 4 sec on an inertial load ergometer. Participants either completed these

bouts consecutively at the end of 8 hours of sitting, or five sprint bouts at the top of each

hour over the 8 hours of sitting. While sitting time did not differ between groups, this

study showed improved PPL on the following day in the individuals who spread the

sprint bouts throughout the day but not in those who completed these bouts consecutively

in the evening (211). The findings suggest that exercise resistance may arise if exercise is

performed in the evening following a day of inactivity but can be avoided if the same

level of activity is spread throughout the day. Interestingly, because daily inactivity and

exercise were similar between groups, this study suggest the development of exercise

resistance may be avoided with regular contraction spread throughout the waking hours.

Regular contraction may be necessary to maintain healthy function and sensitivity to

healthy stimuli. While the mechanisms are yet to be clearly elucidated, the observations

of Lambernd et al (110) may provide insight as they observed that single muscle fibers

treated with TNF- did not show impaired insulin sensitivity if they were also contracted.

It is possible that physical inactivity causes the production of some factor(s) that impair

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normally healthy physiological stimuli, such as exercise, from occurring or being

realized. This hypothesis agrees with the observation that people who exercise regularly,

do not realize the decreased risk of cardiovascular disease or death if they also have

lifestyles characterized by chronic inactivity, or that the exercise needs to be extreme in

order to be protective (e.g.; 60-75 min vigorous each day)(39).

Furthermore, available evidence suggests that this ‘exercise resistance’ may not

be an exclusive phenomenon to just postprandial metabolism. Breen et al. (22) found that

reducing daily steps for two weeks, from ~6,000 steps to ~1400 steps, induced an

‘anabolic resistance’. With the use of muscle biopsies, this study was able to show that

the increase in myofibrillar protein synthesis, after consumption of high-grade protein,

was attenuated by 26% in inactive participants compared to baseline, after 2 weeks of

reducing daily step count.

In light of these new and intriguing findings the interpretation of previous studies

might be reconsidered. It is possible that inactivity impairs other healthy adaptations to

normally effective stimuli. While indirectly contested (131), this new evidence may

provide additional insight by which the concept of non-response to acute and chronic

exercise can, at least partly, be further elucidated. If these hypotheses can be generalized,

it may provide an explanation for any study that found a non-response to a physiological

stimulus in that it might be related to participants having a background of too much

inactivity. It is possible this phenomenon may have been present in previous studies but

has gone largely unrecognized due to a lack of evidence suggesting non-exercise activity

may play a role in the hypothesized response. For example, Rogers et al. (159) found 7

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days of aerobic exercise resulted in a significant improvement in glucose tolerance,

measured as 3-hour area under the curve of plasma glucose excursion, in response to

100g oral glucose tolerance test (OGTT) compared with a non-exercising control. This

improvement occurred in the absence of cardiovascular adaptation and without changes

in body mass or fat content. Thus these authors concluded the changes in glucose

tolerance must be due to persistent effects of the last bout of exercise. These investigators

again studied subjects after 6 months of sedentary, free-living, and found that OGTT after

a single bout of exercise at the same intensity and duration as that was performed in the

previous intervention failed to improve plasma glucose or insulin responses following

acute exercise (159). While this seemed counterintuitive to these authors at the time,

recent evidence, including the emergence of exercise resistance, may shed light on a

possible explanation to results that seemed perplexing by these authors own admission in

1988. It’s possible that the second group of subjects were too inactive to benefit from the

acute bout of exercise.

Possible Mechanisms Inducing Exercise Resistance

Although these previous studies have demonstrated that inactivity, even with

moderate exercise, has deleterious effects on triglyceride and glucose tolerance (1, 38, 98),

none have directly investigated the mechanisms for exercise resistance. Therefore, current

understanding requires speculation. Reduced activity of muscle LPL is perhaps the most

likely explanation for the impaired triglyceride clearance (63). As previously described,

LPL is upregulated post exercise inducing the insertion of additional extra binding sites on

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the muscle’s capillary endothelium (76, 77, 91, 124). Because of the role of LPL as the

rate-limiting enzyme for removing chylomicrons and VLDL triglyceride from the

circulation (201), hydrolysis of triglycerides from these carriers and subsequent uptake by

the muscle would therefore be delayed (55). With the lack of difference in PPL with or

without exercise found in the previous studies (1, 98), it seems inactivity can impair or

completely abolish the exercise-induced upregulation of LPL. In an animal model, physical

inactivity modeled by hind-limb unloading, significantly reduced the in vitro activity of

muscle lipoprotein lipase, and decreased the amount of heparin-released LPL and may

reduce its activity by up to 90% (15, 220). This difference in activity was seen without

changes in LPL mRNA. This suggests the inhibition of LPL is post-transcriptional.

One possible, post-transcriptional mechanistic explanation for this impairment

could be thioredoxin-interacting protein (TXNIP). It was recently observed that 6-h of

hind-limb immobilization in rats resulted in an increase in TXNIP protein expression and

mRNA and a decrease in insulin-stimulated glucose uptake in the soleus muscle (94). It

was speculated that this was possibly due to endocytosis and subsequent decrease in the

amount of GLUT4 at the surface of the sarcolemma (94). It seems possible a similar

process could affect plasma TG clearance by skeletal muscle via downregulation of LPL

on capillary endothelium, either directly or indirectly through manipulation of GPIHBP1

responsible for binding and tethering LPL to the luminal surface of the capillary (12, 17).

Whether the lack of contractile activity in humans that experience severe reductions in

daily steps causes dramatic increases TXNIP remains to be determined. However, the

previously mentioned increase in TXNIP with immobilization protocols can be

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eliminated by activation of AMPK via AICAR administration (94). This adds additional

credence to the hypothesis linking accumulation of TXNIP to lack of a precursor that

senses energy turnover and exercise resistance. Interestingly, both TXNIP and LPL

activity are sensitive to changes in contractile activity, even at low intensity such as those

seen during leisurely walking (e.g. 30% VO2max) (15, 63). Furthermore, AMPK

activation has been shown to also upregulate LPL activity in skeletal muscle (116).

Future studies are needed to test the hypothesis that AMPK activation prevents TXNIP

elevation and allows a healthy increase in GLUT4 and LPL to augment the uptake of

glucose and plasma triglyceride into skeletal muscle.

Future Work

While we know that exercise is beneficial for health and wellness, we are just

learning that inactivity is more than the lack of exercise and it seems to be having a

separate impact on health independent of exercise. This is especially true in light of

emerging evidence suggesting that this exercise may not reduce the risk of developing

chronic disease and premature mortality against a background of inactivity.

Future research should expand on the newfound ‘exercise resistance’ hypothesis

and the nature of this phenomenon, determining if it extends beyond the measures of

PPL. It would be increasingly useful for the medical practitioner, exercise scientist, and

public health professionals to understand this and how to counteract it. If inactivity

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progressively encroaches on adaptations to aerobic exercise in a ‘dose-response’ fashion

or occurs at some threshold, further characterizing this would be invaluable to identify

what minimum level of activity is necessary to fully realize the full and proper responses

to acute and chronic exercise.

Furthermore, while current research is beginning to expound on the inability of

exercise to improve indices of health, much of the work has focused solely on acute bouts

of exercise. It is vital that data be provided to expand on ‘exercise resistance’ and its

presence or absence in response to an accumulated training stimulus. Investigations into

postprandial metabolism have shown great merit and have been shown to be sensitive to

changes induced by inactivity. However, it is also important to investigate if these

adverse responses, including ‘exercise resistance’, extend to other cardiometabolic

responses to exercise training beyond PPL. In doing so, the data provided would provide

substantial evidence which could aid in crafting additional and more tangible guidelines

for health and wellness. The current guidelines are currently limited to generalities such

as “move more, sit less” and “avoid inactivity”. This seems to be a result of suffering

from a lack of quantitative evidence on the effects of inactivity. It is prudent to avoid

development of prescriptive standards without sufficient data to buttress them.

Nevertheless, this only underscores the need for more systematic examination of varying

degrees of inactivity and their effect on markers of health. While these are laudable in

their intent, the lack of definitive, quantitative conclusions on inactivity relegate these

guidelines to subjective interpretation and can cause disparate effects from group to

group, and individual to individual.

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Lastly, whether it be accumulation of TXNIP or some other co-factor within the

muscle, it is important for future research to elucidate the mechanism by which ‘exercise

resistance’ develops and, again, if it is limited mainly to PPL. A mechanistic

understanding of this phenomenon would be of great benefit to practitioners and scientist

who will be tasked with combatting this as our society continues to grow more inactive.

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Appendices

APPENDIX A: METHODOLOGICAL TECHNIQUES

Oxygen Consumption

During exercise the participants breathed through a two-way non-rebreathing

valve (Hans Rudolph, Kansas City, MO). Ventilation was measured via an inspiratory

pneumotachometer attached to the two-way valve (Hans Rudolph, Kansas City, MO).

Expired gas samples were taken from a mixing chamber which was directly connected

via capillary tubing to oxygen and carbon dioxide analyzers (Applied Electrochemistry,

Models S-3A/I and CD-3A, respectively). MOXUS metabolic software (Applied

Electrochemistry) was then used to continuously analyze VO2 and VCO2

Near-Infrared Spectroscopy

Near-infrared spectroscopy (NIRS) (OxiplexTS, ISS Oximeter Model 95205,

Champaign, IL) was used to measure deoxygenated hemoglobin [HHb] during exercise in

a thigh muscle (i.e.; vastus lateralis). NIRS analyzes the chromophores of O2Hb and

HHb, which have different optical properties of absorbing near-infrared (wave length:

690 nm, 830 nm). This enables NIRS to measure the absolute concentrations of O2Hb

and HHb in real-time and noninvasively (21, 57, 136).

Before every test, the NIRS was calibrated after about 30 minutes of warm-up.

Figure 12 shows the description of the probe designed for skeletal muscle measurements

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and this probe was used for this study. The schematic of the near-infrared light

penetrating 2.0 cm, 2.5 cm, 3.0 cm, and 3.5 cm in depth from skin. The acquisition

frequency of 2 Hz was used for this study. NIRS data was continuously monitored and

averaged between minutes 9 and 10 of submaximal exercise tests for data analysis.

Figure 12. Diagram of OxiplexTS probe for measuring deoxygenated hemoglobin in

skeletal muscle during submaximal exercise

Blood Lactate Measurements

Blood lactate concentration was determine using the following procedures and enzymatic

reactions:

Part 1: Supplies, solutions, etc.

Glassware

1. Acupette capillary tubes Qty: 2 per blood sample? 2. Eppendorf 1.5ml tube Qty:((x+3)*2) 3. Polyproylene 12 x 75 mm test tube Qty: (2 per blood sample)

Solutions and Reagents

1. NAD Sigma N-7004

2. LDH Sigma L-3916

3. Hydrazine Sigma H-9507

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4. Glycine Fisher G-46

5. Lactate Std Sigma 826-10

6. Perchloric Acid Fisher A-229 70%

PCA: to get 8%, take 57.14ml of 70% stock, bring to 500ml with dH20

Glycine-Hydrazine Buffer for 1000ml 0.33M glycine 25.02g 0.27M hydrazine 23.98mL Mix and bring up to 1000mL with dH20, pH to 9.2

Part 2: Sample Preparation

Step 1: Prepare Reagent Cocktail

1. Prepare reagent cocktail for each sample or tube

a. 1ml of glycine-hydrazine buffer

b. 0.83mg of NAD

c. 5uL of LDH, if using 1000ul/ml stock, need 5ul

2. If you have X blood samples:

. ((x+3)*2 +1) of the above cocktail recipe

a. Need the samples, one blank, two standards, all in duplicate, plus one extra so you have

enough buffer for all of your samples

Step 2: Blood deproteinization

1. Protective gloves, glasses, and lab coat should be used when handling blood

2. Exactly 0.5mL of whole blood should be immediately mixed with 1.5 mL 8% PCA in

Eppendorf tube

3. Vortex the tube to fully deproteinize the sample

4. Centrifuge at 4degreesC for at least 15min t 3000RPM

5. Transfer the clear supernatant to an appropriately labeled tube

a. Lactate is stable in supernatant for at least one week at 2-6degrees C, longer if frozen

Step 3: Supernatant/ Reagent Mixture 1. Add 1ml of reagent cocktail (see part 2, step 1 above)

2. Add 50uL of 8% PCA to the Eppendorf 1.5mL tubes for the blank

3. Add 50uL of two lactic acid standards to std1 and std2 Eppendorf 1.5ml tube.

4. Add 50uL of sample supernatant to sample 1 to sample N Eppendorf 1.5mL tubes.

5. Vortex each Eppendorf tube

6. Incubate tubes at 37degrees C for 45 min in shaking water bath at 60RPM

Part 3: Sample Analysis

Step 1: Spectrophotometer and Calculations

1. Warm the spectrophotometer for 30 min, read the sample at 340nM

a. Instrument: Spectrophotometer Beckmann DU-600

b. Method: A:\LAT

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c. Read average time: 0.5s

d. Fixed wavelength: 340nM

e. Factor 10.13

2. Calculations

Lactate standard 40mg/100ml, 400mg/L, or 4.44mM (Sigma 826-10, now Trinity Biotech 82610)

i.Low 10mg/100mL (1.11mM)

ii.High 20mg/100mL (2.22mM)

a. Abs/E.C. = Abs/6.22

b. 1.05/0.05 = cuvette dilution (0.05mL blood in 1mL reagent cocktail)

c. Standard concentration = Abs/6.22 x cuvette dilution = Abs x 3.38

d. 3/1 = blood dilution (0.5mL blood in 1.5mL of 8% PCA)

e. Sample concentration = (Abs/6.22) x 1.05/0.05 x 3/1 = Abs x 10.13mM

f. [La] = abs x 10.13mM

Plasma Glucose Measurement

Plasma glucose we measured via spectrophotometry using commercially available

kits (Pointe Scientific, Inc. Canton, USA). The plasma samples were removed from freezer

(-80°C) and thawed. 5 μL of plasma sample is added to 500 μL of glucose hexokinase

reagent and then incubated at room temperature for 3 minutes, following gentle mixing via

a benchtop votex machine. Glucose is phosphorylated with ATP to produce glucose 6-

phosphate (G-6-P) in the reaction catalyzed by hexokinase (HK). The glucose 6-phosphate

is then oxidized via reduction of NAD to NADH in the reaction catalyzed by glucose 6-

phosphate dehydrogenase (G6PDH). The absorbance of NADH formed was measured at

340 nm using a via spectrophotometry (Cary Eclipse Florescence Spectrophotometer,

Agilent Technologies, Santa Clara, California). The concentration of NADH is directly

proportional to the concentration (mg•dL-1) of glucose in the sample.

Plasma Triglyceride Measurement

Plasma triglyceride was measured via spectrophotometry using a commercially

available kit (Pointe Scientific, Inc., Canton, USA). Samples were removed from freezer

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(-80°C) and thawed. 3.5 μL of plasma is then pipetted off and added to 350 μL of pre-

warmed (37°C) triglyceride reagent. These samples are and incubated for 30 minutes on

an oscillating tray in a warm (37°C) oven. The reagent hydrolyzes triglycerides in the

sample via lipase and produces glycerol and free fatty acids. Glycerol is then

phosphorylated by ATP to glycerol 1- phosphate and ADP through a reaction catalyzed

by glycerol kinase (GK). The glycerol 1-phosphate is then oxidized by glycerol

phosphate oxidase (GPO) to yield hydrogen peroxide. The condensation of hydrogen

peroxide with 4-chlorophenol and 4- aminophenazone (4-AA) in the presence of

peroxidase (POD) produces a red colored quinonimine dye. The intensity of the colored

complex formed is directly proportional to the triglycerides concentration of the sample.

The plate is read at 500 nm using a microplate reader (Tecan Infinite 200 PRO, Tecan

Group Ltd., Männedorf, Switzerland).

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APPENDIX B: RESEARCH CONSENT FORMS

Consent for Participation in Research

Title:

The Effect of Prolonged Sitting on Metabolic and Cardiovascular Responses to Short

Term Exercise Training

Introduction

The purpose of this form is to provide you information that may affect your decision as to

whether or not to participate in this research study. The person performing the research

will answer any of your questions. Read the information below and ask any questions

you might have before deciding whether or not to take part. If you decide to be involved

in this study, this form will be used to record your consent.

Purpose of the Study

The purpose of this study is to investigate the effect of daily sitting time on plasma

triglycerides, artery function and other training adaptations resulting from 1.5 weeks of

intense cycle training.

What will you be asked to do?

Before you can be admitted to the study, you will be given brief preliminary tests. This

will include filling out a brief Health Research Questionnaire, and taking measurements

of your height and weight. Only if you are apparently healthy and at low risk for

cardiovascular disease will you be invited to participate in this study. Prior to your

enrollment in the study, your peak oxygen uptake (VO2peak) will be determined while

exercising on a cycle ergometer (lab exercise bike) and also your heart rate during

submaximal cycling will be determined.

Participation will span seventeen days, with periodic visits to the Human Performance

Laboratory (HPL). You will randomly assigned to one of two groups:

Low Sitting Group: Metabolic and cardiovascular responses to exercise program and low

sitting lifestyle (sitting <5h/d, >15,000 steps/d) outside of exercise.

High Sitting Group: Metabolic and cardiovascular responses to exercise program and

high sitting lifestyle (sitting >11h/d, <2,500 steps/d) outside of exercise.

Step-by-Step Protocol:

Pre-Intervention Phase (Week 1)

Day 1: High Fat Tolerance Test (HFTT) and Flow Mediated Dilation 1. Arrival at the Human Performance Laboratory (HPL), informed consent, health history

questionnaires, body mass and height.

2. Flow mediated dilation measurement.

3. Catheter insertion and fasting blood collection.

4. High fat shake consumption

5. Postprandial blood sampling hourly for 6 h (6 additional samples).

6. Expired gas collection for 20 minutes at baseline and 1, 3, 5 h after high fat shake intake.

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7. Post HFTT flow mediated dilation measurement (FMD)

■ Total time: 430 minutes

Day 2: VO2peak test 1. Arrival at HPL, body mass measurement.

2. Warm up for 5 minutes.

3. Perform peak oxygen consumption test; (VO2peak test, 8-12 min.)

4. Installation of the activity monitor.

■ Total time: 30 minutes

Day 3: Submaximal Exercise test

1. Arrival at HPL, body mass measurement.

2. Catheter insertion and baseline blood collection.

3. Warm up for 5 minutes at 50% VO2peak.

4. Perform continuous 15-minute submaximal exercise at 80% VO2peak and collect blood at 15

min.

5. 5 min post exercise blood collections.

Day 4 and 5: Low energy expenditure 1. No Testing will be done on these days but subjects will need to be cognizant of sitting

time and step count to keep it low

Day 6: Initial training bout 1. Arrival at HPL, body mass measurement.

2. 5-minute warm up.

3. 20-minute cycling bout at 80% VO2peak.

4. 10-minute rest interval.

5. Two 5-minute interval exercise bouts at 95% VO2peak, with 5 min rest

■ Total time: 60 minutes

Day 7: High Fat Tolerance Test and Flow Mediated Dilation

(To examine the acute effects of the single bout of exercise performed the day before).

1. Arrival at HPL, body mass and height measurement.

2. Flow mediated dilation measurement.

3. Catheter insertion and fasting blood collection.

4. High fat shake consumption.

5. Postprandial blood sampling hourly for 6 h (6 additional samples) 6. Removal of activity monitor.

7. Expired gas collection for 20 minutes at baseline and 1, 3, 5 h after high fat shake

intake.

8. Post HFTT flow mediated dilation measurement.

9. Reattachment of activity monitor.

105

■ Total time: 420 minutes

Training Phase (Week 2)

Day 1, 3 , 5 & 7: Training Days 1. Arrival at HPL, body mass measurement.

2. 5-minute warm up.

3. 20-minute cycling bout at 80% VO2peak.

4. 10-minute rest interval.

5. Two 5-minute interval exercise bouts at 95% VO2peak.

■ Total time: 240 minutes (60 min each of 4 days)

Post-Intervention Phase (Week 3)

Day 1: High Fat Tolerance Test and Flow Mediated Dilation 1. Arrival at HPL, body mass

2. Flow mediated dilation measurement.

3. Catheter insertion and fasting blood collection.

4. High fat shake consumption.

5. Postprandial blood sampling hourly for 6 h (6 additional samples).

6. Removal of activity monitor.

7. Expired gas collection for 20 minutes at baseline and 1, 3, 5 h after high fat shake intake.

8. Post HFTT flow mediated dilation measurement.

■ Total time: 420 minutes

Day 2: Post-training submaximal exercise test

1. Arrival at HPL, body mass measurement.

2. Catheter insertion and baseline blood collection. 3. Warm up for 5 minutes.

4. Perform continuous 15-minute submaximal exercise at 80% VO2peak (of pretaining VO2peak)

5. Post exercise blood collections.

■ Total time: 40 minutes

Day 3: Post-training VO2peak test. 1. Arrival at the HPL, body mass..

2. Warm up for 5 minutes.

3. Perform continuous peak oxygen consumption test; (VO2peak test, 8-12 min.)

■ Total time: 40 minutes

Total time per subject for both trials is approximately 28h ( 1,680 minutes) over 17

days.

What are the risks involved in this study?

None of the above procedures are expected to be unduly painful or unsafe in healthy

individuals. Maximal and submaximal cycling tests as well as the 30-minute training

106

sessions will all be performed in comfortable environmental conditions (e.g. 20 - 25 º C

and relative humidity of ~ 50%). During VO2peak testing, the last 2-4 minutes of the test

may cause a feeling of fatigue and heavy breathing similar to performing ‘high intensity

interval training’. This feeling of fatigue generally subsides soon after completion (e.g.

within 2-10 minutes). However, with all aerobic exercise, there is a risk of cardiovascular

events. The risk of any sort of cardiovascular complication in apparently healthy (no

documented CV disease) individuals is very low with no complications in 380,000 tests.

Furthermore, in over 35 years involving several thousand exercise sessions, no subject in

the Human Performance Lab has experienced any cardiac event. During each trial, an

AED will be present and a CPR certified test administrator will be present. There is a

small risk of muscular injury or muscular soreness within 24- to 48-hours post-session.

To reduce these risks, a brief warm-up period will be performed.

Blood samples will be drawn during each HFTT and submaximal exercise visit via a

venous catheter in the forearm or antecubital vein. A certified phlebotomist will insert all

catheters. Minor discomfort may occur during the insertion of the catheter. The

discomfort associated with the insertion of the catheter is similar to a venipuncture. Risks

associated with placement of the catheter include bleeding, pain, swelling, bruising,

infection, and thrombophlebitis. Each blood draw will be approximately 6 mL of blood or

the equivalent of 162 mL for the entire experiment. This amount of blood is

approximately 11 tablespoons, and less than 4% of total blood volume. After analysis, if

subjects are found to have abnormally high fasting or post-prandial triglyceride levels,

they will be alerted of this and advised to follow-up with their primary care physician.

The activity monitor will be placed on your leg the day before study participation

commences. Participants may experience a low level annoyance. The activity monitor

will be detached for 30-60 min to load data to a computer. Subjects will carry a

pedometer attached to their waist for a week before the initiation of the first trial for the

estimation of an average daily steps and throughout the trials. It will not give participants

any discomfort.

During the tests, participants may stop performing the task at any time and for any reason

if he or she feels the need to do so. If participants wish to discuss the information above

or any other risks participants may experience, participants may ask questions now or call

the Principal Investigators.

What are the possible benefits of this study?

You will receive no direct benefit from participating in this study; however, each subject

completing the study will be provided with a graphic and verbal description and

explanation on their peak aerobic capacity, heart rate, blood pressure and metabolic

responses both in fasted and non-fasting states in response to different prior physical

activity/inactivity status.

Do you have to participate?

No, your participation is voluntary. You may decide not to participate at all or, if you

start the study, you may withdraw at any time. Withdrawal or refusing to participate will

107

not affect your relationship with The University of Texas at Austin (University) in

anyway.

If you would like to participate please fully read, sign, and return this form to the

principal investigator of this study (Heath Burton). You will receive a copy of this form

for your personal records.

Will there be any compensation?

You will not receive any type of payment participating in this study.

What if you are injured because of the study?

1. The University has no program or plan to provide treatment for research related

injury or payment in the event of a medical problem. In the event of a research

related injury, please contact the principal investigator.

2. The University has no program or plan for continuing medical care and/or

hospitalization for research-related injuries or for financial compensation.

3. If injuries occur as a result of study activity, eligible University students may be

treated at the usual level of care with the usual cost for services at the Student

Health Center, but the University has no program or plans to provide payment in

the event of a medical problem.

How will your privacy and confidentiality be protected if you participate in this

research study?

Each subject will be assigned a unique Subject ID code. This informed consent form and

the Health History Questionnaire are the only places where any personal identifying

information will be recorded. These forms will be stored in a locked file cabinet. In all

other cases, your data will only be identifiable by your unique code. Only the director of

the laboratory (Dr. Coyle) will have access to a master list that will link your identity to

your code.

Because you will be participating in this study and may do so along with other subjects in a

small group, we will ask that you do not disclose names of participants in your group or

any information that was discussed with other group members outside of the experimental

session.

If it becomes necessary for the Institutional Review Board to review the study records,

information that can be linked to you will be protected to the extent permitted by law.

Your research records will not be released without your consent unless required by law or

a court order. The data resulting from your participation may be made available to other

researchers in the future for research purposes not detailed within this consent form. In

these cases, the data will contain no identifying information that could associate it with

you, or with your participation in any study.

If you choose to participate in this study, you may be photographed or video recorded.

Any photographs or video recordings will be stored securely and only the research team

108

will have access to the recordings. Recordings will be kept for 3 years after the research

experiment has been completed and then erased.

Whom to contact with questions about the study?

Prior, during or after your participation you can contact the researcher Heath Burton at

(864)-940-4103 or send an email to heath.burton@utexas.edu for any questions or if you

feel that you have been harmed.

This study has been reviewed and approved by The University Institutional Review

Board and the study number is 2017-07-0074

Whom to contact with questions concerning your rights as a research participant?

For questions about your rights or any dissatisfaction with any part of this study, you can

contact, anonymously if you wish, the Institutional Review Board by phone at (512) 471-

8871 or email at orsc@uts.cc.utexas.edu.

Participation

If you agree to participate please sign and return this form to a member of the

research team.

Signature

You have been informed about this study’s purpose, procedures, possible benefits and

risks, and you have received a copy of this form. You have been given the opportunity

to ask questions before you sign, and you have been told that you can ask other

questions at any time. You voluntarily agree to participate in this study. By signing this

form, you are not waiving any of your legal rights.

Photography and video recording of your sessions is optional. However, if

participants agree to be photographed or video recorded their images may also

be used for professional and educational presentations not related to this

research study.

______ I agree to be photographed and video recorded.

______ I do not want to be photographed and video recorded.

_________________________________

Printed Name

_________________________________ _________________

Signature Date

As a representative of this study, I have explained the purpose, procedures, benefits, and

the risks involved in this research study.

_________________________________

Print Name of Person obtaining consent

_________________________________ _________________

Signature of Person Obtaining Consent Date

109

Consent for Participation in Research

Title:

Dose Response of Physical Inactivity on Plasma Triglyceride Elevation After a Meal.

Introduction

The purpose of this form is to provide you information that may affect your decision as to

whether or not to participate in this research study. The person performing the research

will answer any of your questions. Read the information below and ask any questions

you might have before deciding whether or not to take part. If you decide to be involved

in this study, this form will be used to record your consent.

Purpose of the Study

The purpose of this study is to investigate the effect of two days of reduced daily

stepping, and moderate exercise on plasma triglyceride elevation after a meal.

What will you be asked to do?

Before you can be admitted to the study, you will be given brief preliminary tests. This

will include filling out a brief Health Research Questionnaire, and taking measurements

of your height and weight. Only if you are apparently healthy and at low risk for

cardiovascular disease will you be invited to participate in this study. Prior to your

enrollment in the study, your maximal oxygen uptake (VO2max) will be determined while

running on a treadmill and also your heart rate during submaximal running will be

determined.

Each trial will require five days, with periodic visits to the HPL:

Trial 1: Plasma triglyceride responses with two days of 2,500 steps per day and a single

one-hour bout of exercise on the night of the fourth day.

Trial 2: Plasma triglyceride responses with two days of 5,000 steps per day and a single

one-hour bout of exercise on the night of the fourth day.

Trial 3: Plasma triglyceride responses with two days of 7,500 steps per day and a single

one-hour bout of exercise on the night of the fourth day.

The order of protocols will be randomized.

Step-by-Step Protocol:

Week prior to the initiation: Health history questionnaires, familiarization, VO2max test.

5. Arrival at the Human Performance Laboratory (HPL), informed consent, health history

questionnaires, body mass and height.

6. Installation of the activity monitor.

7. Perform resting gas measurement.

8. Warm up for 5 minutes on a treadmill.

9. Perform submaximal exercise test with four treadmill speeds lasting five minutes each. The

intensity will approximate 40, 60, 70 and 80% of age predicted maximal heart rate.

10. Recover for ~ 15-20 minutes (re-hydrate to pre-exercise bodyweight)

11. Perform continuous maximal oxygen consumption test; (VO2max test, 8-12 min.)

110

■ Total time: 150 minutes

Trial sessions

Control Phase: Control Day 1 and 2 (C1 and C2)

Day prior to Control day 1: Activity monitor installation

1. Arrival at the laboratory any time before 17:00 h.

2. Installation of activity monitor

- Total time: 30 minutes

■ Total time spent during Control Phase: 30 minutes

Intervention Phase: D1

Day 1: Reduced daily steps

1. Sitting in preferred place to accommodate step reductions (not necessarily in HPL).

Day 2: Reduced daily steps & 1-hr treadmill running

1. Sitting in preferred place to accommodate step reductions (not necessarily in HPL).

2. Arrival at HPL at 16:50 h.

3. Exercise at 65%VO2max for one hour at around 17:00 h

4. Dinner provided in the laboratory.

- Total time: 120 min

Day 3: High fat tolerance test and resting fat oxidation

8. Arrival at HPL, body weight, 8:00 h.

9. Catheter insertion and fasting blood collection.

10. High fat shake intake

11. Postprandial blood sampling hourly for 6 h (6 additional samples)

12. Expired gas collection for 10 minutes at 2, 4, 6 h after high fat shake intake

13. Detachment of the activity monitor

- Total time: 420 minutes

■ Total time spent during Intervention Phase:

- All Trials: 600-700 minutes

Total time per subject for all trials is approximately 1900 minutes

What are the risks involved in this study?

111

None of the above procedures are expected to be unduly painful or unsafe in healthy

individuals. The maximal oxygen uptake (VO2max), submaximal tests, and 1 hour of

moderate exercise at 65% VO2max will be performed at 20 - 25 º C and relative

humidity of ~ 50%. During VO2max test, only the final 2 to 4 minutes of the test is at

or near maximal levels of exertion and thus accompanied by a sensation of leg fatigue

and heavy breathing. This moderate feeling of fatigue will subside soon after

completion (i.e.; 2-10 min). There is a very small risk that participants could

experience a muscular injury, such as muscle strain. It is also possible that muscle

soreness may develop 24 to 48 hours after any given testing session. To help reduce

these risks, a warm up session will be mandatory prior to performing these tests.

Blood samples will be drawn during each HFTT via venous catheter in an antecubital

vein. A certified phlebotomist will insert the catheters. Minor discomfort may occur

during the insertion of the catheter. The discomfort associated with the insertion of

the catheter is similar to a venipuncture. Risks associated with placement of the

catheter include bleeding, pain, swelling, bruising, infection, and thrombophlebitis.

Approximately 42 ml of blood will be drawn per trial. Over the course of the entire

study approximately 126 ml of blood will be drawn. This sample volume is

approximately 2.3 % of the individual’s total blood volume. If participants are found

to have abnormally high triglyceride levels, they will be alerted of this and advised to

follow-up with their primary care physician.

The risk of any sort of cardiovascular complication in apparently healthy (no

documented CV disease) individuals is very low, with no complications in numerous

tests. Furthermore, in over 36 years involving more than 50,000 exercise sessions, no

subject in the Human Performance Lab has experienced any cardiac event. The

laboratory is currently equipped with AED. A CPR certified member of the research

team will be present during all testing visits in the unlikely event of an adverse

reaction. During the tests, participants may stop performing the task at any time and for any reason if

he or she feels the need to do so. If participants wish to discuss the information above or any

other risks participants may experience, participants may ask questions now or call the

Principal Investigators.

What are the possible benefits of this study?

You will receive no direct benefit from participating in this study. However, each subject

completing the study will be provided with a graphic and verbal description and

explanation on their maximal aerobic capacity, heart rate, blood pressure and metabolic

responses both in fasted and non-fasting states in response to different prior physical

activity/inactivity status.

Do you have to participate?

112

No, your participation is voluntary. You may decide not to participate at all or, if you

start the study, you may withdraw at any time. Withdrawal or refusing to participate will

not affect your relationship with The University of Texas at Austin (University) in

anyway.

If you would like to participate please fully read, sign, and return this form to the

principal investigator of this study (Heath Burton). You will receive a copy of this form

for your personal records.

Will there be any compensation?

You will not receive any type of payment participating in this study.

What if you are injured because of the study?

4. The University has no program or plan to provide treatment for research related

injury or payment in the event of a medical problem. In the event of a research

related injury, please contact the principal investigator.

5. The University has no program or plan for continuing medical care and/or

hospitalization for research-related injuries or for financial compensation.

6. If injuries occur as a result of study activity, eligible University students may be

treated at the usual level of care with the usual cost for services at the Student

Health Center, but the University has no program or plans to provide payment in

the event of a medical problem.

How will your privacy and confidentiality be protected if you participate in this

research study?

Each subject will be assigned a unique Subject ID code. This informed consent form and

the Health History Questionnaire are the only places where any personal identifying

information will be recorded. These forms will be stored in a locked file cabinet. In all

other cases, your data will only be identifiable by your unique code. Only the director of

the laboratory (Dr. Coyle) will have access to a master list that will link your identity to

your code.

Because you will be participating in this study and may do so along with other subjects in a

small group, we will ask that you do not disclose names of participants in your group or

any information that was discussed with other group members outside of the experimental

session.

If it becomes necessary for the Institutional Review Board to review the study records,

information that can be linked to you will be protected to the extent permitted by law.

Your research records will not be released without your consent unless required by law or

a court order. The data resulting from your participation may be made available to other

researchers in the future for research purposes not detailed within this consent form. In

these cases, the data will contain no identifying information that could associate it with

you, or with your participation in any study.

113

If you choose to participate in this study, you may be photographed or video recorded.

Any photographs or video recordings will be stored securely and only the research team

will have access to the recordings. Recordings will be kept for 3 years after the research

experiment has been completed and then erased.

Whom to contact with questions about the study?

Prior, during or after your participation you can contact the researcher Heath Burton at

(864)-940-4103 or send an email to heath.burton@utexas.edu for any questions or if you

feel that you have been harmed.

This study has been reviewed and approved by The University Institutional Review

Board and the study number is:

2018-08-0031

Whom to contact with questions concerning your rights as a research participant?

For questions about your rights or any dissatisfaction with any part of this study, you can

contact, anonymously if you wish, the Institutional Review Board by phone at (512) 471-

8871 or email at orsc@uts.cc.utexas.edu.

Participation

If you agree to participate please sign and return this form to a member of the

research team.

Signature

You have been informed about this study’s purpose, procedures, possible benefits and

risks, and you have received a copy of this form. You have been given the opportunity

to ask questions before you sign, and you have been told that you can ask other

questions at any time. You voluntarily agree to participate in this study. By signing this

form, you are not waiving any of your legal rights.

Photography and video recording of your sessions is optional. However, if

participants agree to be photographed or video recorded their images may also

be used for professional and educational presentations not related to this

research study. Therefore, these may be kept indefinitely.

______ I agree to be photographed and video recorded.

______ I do not want to be photographed and video recorded.

_________________________________

Printed Name

_________________________________ _________________

Signature Date

As a representative of this study, I have explained the purpose, procedures, benefits, and

the risks involved in this research study.

_________________________________

Print Name of Person obtaining consent

114

_________________________________ _________________

Signature of Person obtaining consent Date

115

APPENDIX C: HEALTH HISTORY QUESTIONNAIRE

HEALTH HISTORY QUESTIONNAIRE

HUMAN PERFORMANCE LABORATORY – THE UNIVERSITY OF TEXAS

Subject ID:____________

Date of Birth (mm/dd/yy) ____________________________

Age: ________________________

MALE _____ FEMALE ____

Height ___________ Weight ___________

116

HEALTH HISTORY QUESTIONNAIRE

HUMAN PERFORMANCE LABORATORY – THE UNIVERSITY OF TEXAS

Subject ID:____________

GENERAL HEALTH QUESTIONS

1. Are you taking any of the following medications on a regular basis? Y / N

(Psychotropics, Antihistamines, Asthma Meds, Aldomet, Clonidine,Anti-Depressants,

Anti-Anxiety Meds)

2. Any over-the-counter meds? Y / N

If yes, explain:

3. Do you have any disability or impairment that affects physical performance? Y / N

4. Have you ever had any broken bones, surgery or injury to your lower extremities? Y/N

If yes, explain:

5. Have you had any significant medical problems within the last 10 years? Y / N

If yes, explain:

6. Do you have any drug and/or alcohol dependence? Y / N

If yes, explain:

7. Do you have any heart problems or coronary artery disease? Y / N

If yes, explain.

8. Do you have hypertension (high blood pressure)? Y / N

If yes, explain.

9. Do you have any lung or respiratory problems? Y / N

117

If yes, explain.

10. Do you, or have you previously had a history of blood clotting issues Y / N

If yes, explain.

11. Have you been diagnosed with diabetes? Y / N

12. Are you currently pregnant? Y / N

13. Do you smoke? Y / N

If yes, pattern.

14. Do you use alcohol? Y / N

If yes, pattern.

15. Do you use caffeine (cola, coffee, etc…)? Y / N

If yes, pattern.

16. Do you have any allergies that require medication? Y / N

If yes, explain.

17. Do you experience difficulty swallowing medications or vitamins? Y / N

If yes, explain.

18. Do you take any dietary supplements to increase your exercise performance? Y / N

If yes, what supplements so you normally take?

19. Have you been diagnosed with an obstructive disease of the gastrointestinal tract

including but not limited to esophageal stricture, diverticulous, inflammatory bowel

disease (IBD), peptic ulcer disease, Crohn’s disease,ulcerative colitis, and previous

gastro-esophageal surgery. Y / N

HAVE YOU EVER HAD ANY SIGNIFICANT SYMPTOMS ASSOCIATED

118

WITH EXERCISE?

1. Easy fatigability or prolonged fatigue after exercise? Y / N

If yes, explain.

2. Persistent chest pain during and/or after exercise? Y / N

If yes, explain.

3. Fainting or loss of consciousness during exercise? Y / N

If yes, explain.

4. Palpitations (rapid, irregular, or skipped heartbeats) during exercise? Y / N

5. Have you ever been told to give up sports because of a health problem? Y / N

PHYSICAL TRAINING HISTORY

How many years have you been training?

_______________________________________________________________________

What type of physical training do you participate in?

_______________________________________________________________________

Describe in general, the type of training you have performed for each of your years of

training.

1st

2nd

3rd

4th

5th

6th

119

7th

8th

others

What is your personal best race time (if more than one please list distance, time and type)

________________________________________________________________________

________________________________________________________________________

________________________________________________________________________

________________________________________________________________________

___________________________________________________________________

PLEASE GENERALLY DESCRIBE YOUR TRAINING PROGRAM DURING

THE LAST 6 MONTHS

Type of training:

_______________________________________________________________________

_______________________________________________________________________

_______________________________________________________________________

Average time spent or work done (i.e.; distance):

_______________________________________________________________________

_______________________________________________________________________

_______________________________________________________________________

General Intensity:

120

_______________________________________________________________________

_______________________________________________________________________

_______________________________________________________________________

121

AP

PE

ND

IX D

: A

DD

ITIO

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L T

AB

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S F

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Y 1

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ent

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3942

792

Tab

le 1

0. A

ver

age

Dai

ly S

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both

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1-d

ay i

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ly s

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y d

iffe

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ps

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signif

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nt

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12 a

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14 w

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ent

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s M

SE

.

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15852

2045

6618

873

D1

2

18524

2481

6185

782

D1

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15231

3044

4898

774

D1

0

17820

2255

6414

1182

D9

17913

2341

4561

1022

D8

15734

885

3884

577

D7

11096

1361*

2808

170

D6

18076

2184

5392

963

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1110

3664

489

D4

13787

1339

4071

844

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122

Triglycerides High Step Low Step

AUCT AUCI AUCT AUCI

Baseline 886.8 79.6 322.9 67.2 929.4 73.3 295.9 40.3

Acute Exercise 760.9 73.7† 221.7 49.7* 967.5 98.6 291.6 66.5

Post Training 762.2 65.5† 236.7 61.4* 890.4 64.9 257.8 27.0

Note: Data are presented as MSE. (*) Significantly different from Baseline, p<0.05. (†) significantly different from baseline,

p<0.01

Table 11. Total and incremental areas under the curve of plasma triglyceride

concentrations during HFTTs at Baseline, following a single bout of exercise (Acute),

and following 5 training bouts over the 9-days of training (Post Training). (*)

Significantly different from Baseline, p<0.05. (†) significantly different from Baseline,

p<0.01. All Data are reported as Mean SE.

123

Table 12. Total and incremental areas under the curve of plasma glucose concentrations

during HFTTs at Baseline, following a single bout of exercise (Acute), and following 5

training bouts over the 9-days of training (Post Training). All Data are reported as Mean

SE.

Glucose High Step Low Step

AUCT AUCI AUCT AUCI

Baseline 726.335.1 146.0 38.6 704.326.2 145.031.2

Acute Exercise 701.719.3 148.628.0 758.858.6 180.747.5

Post Training 693.820.9 142.821.6 741.348.4 160.235.9

Note: Data are presented as MSE

124

High Step Hours Postprandial

Baseline H1 H2 H3 H4 H5 H6

Triglycerides (mg/dL)

Baseline 94.0 3.5 144.9 6.2 163.3 21.4 174.4 23.2 152.4 14.6 138.8 9.5 131.9 9.7

Acute Exercise 89.9 6.6 114.4 7.3† 138.9 18.6* 148.6 21.2* 134.7 13.8 123.9 10.8 111.2 8.8

Post Training 87.6 5.6 120.7 6.8† 145.2 17.9 150.7 20.2 133.5 11.6 116.9 7.3* 102.8 6.8*

Glucose (mg/dL) Baseline 97.44.1 -- 127.58.9 -- 133.39.2 -- 107.37.1

Acute Exercise 92.64.9 -- 118.95.0 -- 129.19.4 -- 113.04.8

Post Training 93.86.4 -- 118.88.5 -- 122.73.8 -- 117.06.6

Note: Data are presented as MSE. (*) significantly different from Baseline, p< 0.05. (†) significantly different from Baseline, p< 0.01.

Table 13. Temporal Responses of plasma triglyceride concentration for High Step

treatment during HFTT at Baseline, following a single bout of exercise (Acute), and

following 5 training bouts over the 9-days of training (Post Training). (*) significantly

different from Baseline, p< 0.05. (†) significantly different from Baseline, p< 0.01. Data

reported MeanSE.

125

Low Step Hours Postprandial

Baseline H1 H2 H3 H4 H5 H6

Triglycerides (mg/dL)

Baseline 105.6 6.7 140.5 7.4 167.7 18.3 184.6 19.9 164.7 13.9 149.7 12.8 138.8 11.8

Acute Exercise 112.6 11.0 148.5 12.4 175.3 22.2 191.6 23.9 171.5 18.0 154.5 15.2 139.2 15.9

Post Training 105.5 7.3 140.4 7.4 162.6 14.5 176.6 14.0 155.4 16.4 140.3 11.4 124.7 11.0

Glucose (mg/dL)

Baseline 93.33.5 -- 118.27.1 -- 126.78.6 -- 121.210.5

Acute Exercise 97.83.9 -- 135.6+12.5 -- 135.514.7 -- 118.88.6

Post Training 97.12.7 -- 129.211.4 -- 134.511.3 -- 116.79.6

Note: Data are presented as MSE.

Table 14. Temporal Responses of plasma triglyceride concentration for Low Step

treatment during High Fat Tolerance Test at Baseline, following a single bout of exercise

(Acute), and following 5 training bouts over the 9-days of training (Post Training). Data

reported MeanSE.

126

APPENDIX E: ADDITIONAL TABLES & FIGURES FOR STUDY 2

Figure 13. Average daily steps were measured via activPal activity monitor, attached on

the participant’s anterior thigh throughout each trial. Average daily step counts for each

trial are presented for Control (C1 & C2) and Intervention Phases (D1 & D2). (*)

significantly different from Low, p<0.05. (**) significantly different from Low, p<0.01.

(†) significantly different from Low & Limited step trial, p<0.05.

C1 C2 D1 D20

5000

10000

15000

20000

Day of Trial

Nu

mb

er o

f S

tep

sDaily Steps

Low

Limited

Normal† †

* **

127

Trial AUCT AUCI

Triglycerides

Low 881.3 70.7 342.3 47.8

Limited 835.1 73.7 348.6 48.9

Normal 751.2 54.6† 267.5 39.2*

Glucose

Low 709.1 46.8 159.2 35.2

Limited 678.7 37.7 126.4 25.8

Normal 683.0 23.6 124.9 20.6

Table 15. Total and Incremental areas under the curve of plasma triglyceride & glucose

concentrations during HFTT for each trial. (*) Significantly different from Low &

Limited step group, p<0.05. (†) Significantly different from Low step group, p<0.01 Data

reported MeanSE.

128

APPENDIX F: BIHOURLY RER MEASUREMENTS

Study 1

Pairwise comparisons (Tables 9 &10) for time points during the HFTT indicated

significant differences during the second hour for RER, percent fat and percent

carbohydrate oxidation.

High Step Trial

Baseline Acute Post Training

Respiratory Exchange Ratio

Baseline 0.760 0.01 0.759 0.02 0.774 0.01

Hour 2 0.890 0.01† 0.833 0.01* 0.846 0.01

Hour 4 0.819 0.01 0.780 0.01 0.793 0.01

Hour 6 0.777 0.01 0.766 0.01 0.771 0.01

Percent Fat Oxidation

Baseline 80.6 4.58 81.1 5.87 75.7 4.57

Hour 2 37.5 4.71† 57.0 4.13* 52.7 3.71

Hour 4 61.9 3.20 75.1 3.57 72.2 3.68

Hour 6 76.1 3.39 80.0 3.32 77.0 2.87

Percent CHO Oxidation

Baseline 19.4 4.58 18.9 5.87 24.3 4.57

Hour 2 62.5 4.71† 43.0 4.13* 47.3 3.71

Hour 4 38.1 3.20 24.9 3.57 27.8 3.68

Hour 6 23.9 3.39 20.0 3.32 23.0 2.87

Note: Data are presented as MSE

Table 16. Average postprandial substrate oxidation at each measurement for HS

Treatment group. (*) Significantly different from Baseline, (p<0.05). All Data are

reported as Mean SE.

129

Low Step Trial

Baseline Acute Post Training

Respiratory Exchange Ratio

Baseline 0.773 0.01 0.754 0.01 0.760 0.02

Hour 2 0.879 0.01 0.879 0.01 0.861 0.01

Hour 4 0.826 0.02 0.793 0.1 0.814 0.01

Hour 6 0.789 0.02 0.779 0.01 0.763 0.01

Percent Fat Oxidation

Baseline 76.2 4.44 86.9 3.19 80.6 5.14

Hour 2 41.4 3.83 41.4 2.52 47.3 3.02

Hour 4 59.5 5.26 70.7 4.06 68.3 6.74

Hour 6 72.2 5.39 75.6 4.91 76.1 2.95

Percent CHO Oxidation

Baseline 23.8 4.44 13.1 3.19 19.4 5.14

Hour 2 58.6 3.83 58.6 2.52 52.7 3.02

Hour 4 40.5 5.26 29.3 4.06 31.7 6.74

Hour 6 27.8 5.39 24.4 4.91 23.9 2.95

Note: Data are presented as MSE

Table 17. Average postprandial substrate oxidation at each measurement for LS

Treatment group. All Data are reported as Mean SE.

130

Study 2

Postprandial Measurements Trial

Low Limited Normal

Respiratory Exchange Ratio

Baseline 0.782 0.010 0.750 0.007 0.739 0.006

Hour 2 0.844 0.018 0.833 0.014 0.793 0.011*

Hour 4 0.789 0.016 0.781 0.019 0.764 0.015

Hour 6 0.776 0.015 0.755 0.012 0.738 0.012

Percent CHO Oxidation

Baseline 25.60 3.52 14.74 2.23 11.95 1.77

Hour 2

46.89 6.13 43.14 4.70 29.39 3.89*

Hour 4 28.05 5.58 25.12 6.49 18.09 5.48

Hour 6 23.62 5.09 16.52 4.17 11.26 4.16

Percent Fat Oxidation

Baseline 74.40 3.52 85.26 2.23 88.05 1.77

Hour 2 53.11 6.13 56.86 4.70 70.61 3.89*

Hour 4 71.95 5.58 74.88 6.49 81.91 5.48

Hour 6 76.38 5.09 83.48 4.18 88.74 4.16

Note: Data are presented as MSE (*) significantly different from both Low & Limited, p<0.05.

Table 18. Average postprandial substrate oxidation at each measurement for each trial.

(*) significantly different from both Low & Limited, p<0.05. All Data are reported as

Mean SE.

131

APPENDIX G: STUDY 1 INDIVIDUAL DATA TABLES

Biographical and VO2peak Data

LS Age Height Mass VO2peak

(ml/min)

VO2peak

(ml/kg/min)

Pre Post Pre Post

1 20 157.5 71.3 2364 2409 33.2 33.2

2 21 168.9 74.2 2864 3320 38.6 45.2

3 24 162.6 70.8 1547 1719 21.9 24.0

4 21 172.7 79.9 2684 2986 33.6 37.0

5 26 177.8 68.5 3242 3376 47.3 50.3

6 21 162.6 62.2 1792 1788 28.8 28.7

7 32 157.5 59.1 1431 1449 24.2 24.7

8 25 177.8 94.9 2877 3089 30.3 33.0

HS Age Height Mass VO2peak

(ml/min)

VO2peak

(ml/kg/min)

Pre Post Pre Post

1 19 154.9 60.6 2321 2594 38.3 41.4

2 35 167.6 94.1 2419 2569 25.7 27.6

3 21 175.3 90.2 3875 3888 43.0 43.4

4 26 167.6 91.25 2459 2517 26.9 28.3

5 19 167.6 69.6 3126 3560 44.9 50.7

6 18 177.8 68.25 2942 3098 43.1 46.1

7 26 157.5 45.5 1312 1596 28.8 35.4

8 23 162.6 75.9 1659 1746 21.9 22.6

132

Submaximal Exercise Data

LS

Work

Rate

(W) VO2

%VO2peak Heart Rate Blood Lactate

Rating of

Perceived

Exertion

Pre Post Pre Post Pre Post Pre Post

1 115 1834 77.6 76.1 184 182 7.4 7.5 16 14

2 164 2356 82.2 71 180 173 7.5 8.1 17 13

3 75 1269 82.0 73.8 183 184 5.8 5.1 14 15

4 148 2147 80.0 71.9 196 186 6.3 6.2 17 15

5 175 2482 76.6 73.5 187 179 6.6 6.3 16 12

6 91 1433 79.9 80.2 198 182 7.6 7.7 16 15

7 70 1153 80.6 79.5 167 169 8.8 8.8 14 14

8 167 2306 80.1 74.6 152 151 7.8 7.8 16 18

HS

Work

Rate

(W) VO2

%VO2peak Heart Rate Blood Lactate

Rating of

Perceived

Exertion

Pre Post Pre Post Pre Post Pre Post

1 119 1804 77.7 69.5 185 177 7.8 6.5 16 14

2 132 1915 79.1 74.5 182 171 6.3 4.1 17 12

3 222 3086 79.6 79.4 178 170 8.7 7.8 17 16

4 130 1808 73.5 71.8 156 151 5.7 5.0 14 13

5 176 2504 80.1 70.3 200 189 11.2 10.1 17 16

6 165 2373 80.7 76.6 194 179 8.4 6.1 17 13

7 55 1010 77.0 63.3 181 156 4.3 5.7 11 13

8 82 1306 78.7 74.8 175 157 8.6 8.0 16 13

133

Daily Steps

Day of

LS

Participant #

1 2 3 4 5 6 7 8

4 2489 2026 2446 4133 1576 7162 7836 4903

5 2964 2130 4484 5564 1948 2926 5314 3979

6 3821 4196 6190 3681 8420 7238 8748 840

7 2004 3012 3255 2412 2622 3008 2651 3501

8 2488 2564 4684 5281 1816 5894 5564 2783

9 3602 2943 1352 3821 8066 9902 3936 2867

10 4019 3620 11626 2013 8802 5194 9734 6305

11 5473 2751 3201 5471 7198 2056 8364 4674

12 5824 2416 8150 6882 7956 6768 8164 3325

13 5722 2003 9766 7668 8984 5384 5616 7802

14 2855 2366 1600 4105 8418 6006 3478 2709

Day of

HS

Participant #

1 2 3 4 5 6 7 8

4 10855 13664 10756 20844 8800 15544 16002 13828

5 11226 15886 13598 15802 8436 18244 15862 12630

6 15896 15242 12584 21266 28880 24314 15578 10850

7 17888 7896 8619 7926 15811 11829 8243 10557

8 14956 12886 18286 15896 19210 17500 14996 12142

9 14502 18952 30254 15890 16150 24584 13436 9538

10 10661 11003 27204 16194 17816 26742 19022 13918

11 11285 8004 32422 12570 8994 23268 16638 8670

12 8656 15679 32974 21572 17368 18840 19002 14104

13 9984 14898 17830 16824 15850 27264 16284 7884

14 12061 15017 25292 18664 21164 21866 19322 14904

134

Plasma Triglyceride Concentrations

Participant

#

LS TG Postprandial Time (Hours)

Baseline H1 H2 H3 H4 H5 H6

Baseline

1 66.7 107.9 108.8 107.9 101.9 84.8 85.0

2 119.5 148.1 168.5 211.3 200.0 172.0 144.0

3 118.1 157.7 188.9 204.5 195.9 176.8 157.6

4 109.3 158.5 189.4 207.9 189.2 183.5 177.9

5 99.9 125.1 131.9 151.6 162.4 146.9 131.3

6 92.1 116.5 122.5 115.6 105.6 109.0 112.4

7 122.1 149.7 158.9 201.2 179.1 180.4 181.7

8 116.8 160.4 272.6 276.9 183.5 144.3 120.3

Acute

1 58.2 94.7 95.4 97.8 93.3 87.9 75.4

2 109.8 134.5 140.9 177.2 173.4 146.1 118.8

3 158.0 198.7 221.1 231.0 195.5 187.2 178.9

4 124.8 184.0 224.9 255.8 225.7 216.3 206.9

5 110.4 137.0 145.3 149.3 139.0 126.6 114.3

6 91.2 114.2 118.9 120.3 122.3 115.0 107.7

7 145.5 168.8 173.7 208.5 180.7 181.0 181.3

8 103.2 156.4 282.4 292.6 242.3 176.1 130.2

Post Training

1 83.2 124.8 120.9 136.4 123.1 110.6 77.4

2 83.2 139.5 177.5 150.8 110.2 114.8 119.4

3 122.2 155.5 170.5 211.3 210.0 186.4 162.8

4 128.0 172.2 198.2 208.1 201.9 162.8 123.6

5 93.1 118.2 125.0 155.6 121.8 119.0 116.3

6 86.6 111.0 117.0 141.1 100.1 103.5 106.9

7 122.3 149.8 159.1 164.8 172.3 173.9 175.6

8 125.7 151.9 232.6 245.0 203.7 151.2 115.7

135

Participant

#

HS TG Postprandial Time (Hours)

Baseline H1 H2 H3 H4 H5 H6

Baseline

1 78.0 133.2 145.5 157.8 133.9 120.5 125.5

2 109.2 185.6 311.9 335.6 250.4 202.5 195.2

3 99.3 139.4 139.6 139.5 127.8 128.1 114.4

4 101.8 152.8 158.0 159.0 146.0 133.5 118.9

5 96.6 138.4 136.5 149.2 148.9 144.9 142.8

6 85.3 134.5 130.5 145.8 125.5 126.8 130.1

7 90.1 136.7 139.9 146.0 129.6 121.3 113.0

8 91.5 139.0 144.9 162.5 156.8 132.9 114.9

Acute

1 56.2 91.1 102.1 105.1 84.2 77.8 75.0

2 109.5 148.5 260.7 290.6 199.5 157.3 125.0

3 82.3 103.3 110.3 114.6 102.7 115.1 111.2

4 104.2 111.9 122.4 129.0 139.4 120.5 113.9

5 104.9 132.8 143.7 151.5 153.7 140.1 132.1

6 96.5 104.3 114.4 124.8 123.1 115.9 104.7

7 71.3 93.5 103.1 117.0 102.4 94.9 79.4

8 94.1 129.9 154.4 156.4 172.3 169.4 148.0

Post Training

1 57.7 91.5 104.7 75.3 77.0 75.6 66.3

2 79.9 153.8 261.9 276.0 192.4 149.7 127.9

3 83.9 105.0 108.8 121.8 110.3 108.8 106.3

4 93.5 127.9 138.7 159.7 134.5 123.4 117.2

5 111.9 137.4 161.5 155.1 147.2 124.5 109.1

6 100.4 119.8 136.6 142.6 132.6 119.0 110.5

7 87.7 113.0 120.0 142.0 127.4 112.1 84.4

8 85.9 117.6 129.4 133.2 146.2 122.4 100.4

136

Plasma Glucose Concentrations

Participant

#

LS Glucose Postprandial Time (Hours)

Baseline H2 H4 H6

Baseline

1 92.7 126.9 169.7 132.5

2 93.6 97.2 112.1 88.7

3 77.7 160.4 95.7 92.5

4 83.0 116.2 114.5 166.5

5 101.8 105.7 121.9 96.5

6 109.8 127.6 144.9 122.0

7 96.1 106.0 110.1 112.6

8 91.7 105.5 144.7 158.5

Acute

1 90.9 131.8 158.6 143.6

2 93.1 103.1 119.0 90.4

3 99.3 131.4 123.2 112.4

4 87.0 121.9 122.4 134.5

5 101.3 112.1 129.4 103.1

6 116.2 203.4 228.5 160.2

7 110.4 106.8 98.7 105.7

8 84.5 174.4 104.1 100.6

Post Training

1 91.0 121.0 178.3 128.1

2 88.8 93.0 90.3 82.1

3 95.8 106.5 121.2 145.1

4 100.5 140.6 144.9 159.6

5 89.0 104.1 98.2 89.3

6 109.5 196.2 175.6 104.8

7 96.1 129.5 133.9 102.1

8 106.1 142.9 134.0 122.2

137

Participant

#

HS Glucose Postprandial Time (Hours)

Baseline H2 H4 H6

Baseline

1 103.3 124.3 125.4 107.9

2 103.2 111.2 122.6 99.7

3 86.5 101.8 115.2 97.1

4 104.8 111.8 121.9 120.2

5 114.9 176.0 125.6 91.2

6 94.2 155.5 187.4 147.9

7 78.3 124.9 157.6 84.0

8 94.2 114.9 110.7 110.2

Acute

1 89.5 112.4 122.1 108.8

2 98.1 112.5 184.9 92.8

3 83.1 94.1 99.8 130.9

4 100.2 120.1 123.3 113.1

5 104.5 142.9 101.4 97.3

6 106.4 128.3 137.2 112.7

7 64.2 123.9 134.9 128.3

8 95.1 117.4 129.5 120.2

Post

Training

1 76.1 81.4 131.9 121.4

2 95.8 151.9 118.3 113.2

3 70.0 93.3 124.4 81.3

4 98.8 132.2 120.8 123.9

5 125.5 143.6 101.1 107.8

6 90.0 120.6 134.7 123.6

7 84.4 111.6 131.4 148.1

8 110.1 115.5 119.1 116.6

138

RER Data

Participant

#

LS RER Postprandial Time (Hours)

Baseline H2 H4 H6

Baseline

1 0.78 0.9 0.88 0.86

2 0.82 0.85 0.88 0.81

3 0.8 0.87 0.79 0.77

4 0.72 0.93 0.83 0.81

5 0.75 0.85 0.78 0.77

6 0.79 0.89 0.82 0.73

7 0.75 0.86 0.8 0.77

Acute

1 0.77 0.88 0.83 0.84

2 0.78 0.87 0.81 0.82

3 0.72 0.9 0.8 0.76

4 0.72 0.9 0.8 0.76

5 0.75 0.85 0.78 0.77

6 0.79 0.89 0.73 0.73

7 0.75 0.86 0.8 0.77

Post Training

1 0.81 0.9 0.89 0.77

2 0.77 0.88 0.8 0.78

3 0.72 0.86 0.79 0.74

4 0.78 0.84 0.8 0.79

5 0.73 0.86 0.81 0.76

6 0.71 0.86 0.81 0.71

7 0.8 0.83 0.8 0.79

139

Participant

#

HS RER Postprandial Time (Hours)

Baseline H2 H4 H6

Baseline

1 0.76 0.89 0.79 0.77

2 0.79 0.91 0.85 0.81

3 0.74 0.92 0.84 0.8

4 0.71 0.88 0.79 0.77

5 0.81 0.94 0.83 0.79

6 0.78 0.84 0.8 0.77

7 0.73 0.85 0.83 0.73

Acute

1 0.8 0.83 0.81 0.78

2 0.77 0.89 0.81 0.75

3 0.73 0.82 0.75 0.72

4 0.71 0.85 0.77 0.76

5 0.83 0.79 0.79 0.8

6 0.71 0.84 0.79 0.77

7 0.76 0.81 0.74 0.78

Post Training

1 0.77 0.8 0.79 0.74

2 0.77 0.84 0.8 0.79

3 0.83 0.87 0.76 0.81

4 0.77 0.85 0.8 0.77

5 0.81 0.89 0.81 0.76

6 0.74 0.84 0.77 0.75

7 0.73 0.83 0.82 0.78

140

Postprandial Fat Oxidation (kcal/6h)

High Step

1 2 3 4 5 6 7

Baseline 282.3 252.2 284.7 362.4 280.2 382.1 327.1

Acute 312.2 277.8 446.2 444.4 433.8 388.8 385.4

Post

Training 403.9 295.3 360.1 385.3 372.6 427.2 337.6

Low Step

1 2 3 4 5 6 7

Baseline 248.8 261.0 430.0 187.0 420.3 419.0 346.7

Acute 245.8 269.7 312.8 275.2 548.1 474.7 336.2

Post

Training 298.2 287.2 418.1 299.0 491.1 305.6 355.8

141

APPENDIX H: STUDY 2 INDIVIDUAL DATA TABLES

Biographical & Exercise Data

Participant

# Age Height Mass

VO2max

(ml/min)

VO2max

(ml/kg/min)

Exercise

VO2 %VO2max

Heart

Rate RPE Speed

1 20 71 94.1 3834 40.7 2438 63.6 134 12 4.3

2 22 71 86.4 3999 46.3 2599 64.8 172 13 5.5

3 32 61 54.6 1980 36.3 1287 65.2 139 10 4.4

4 24 73 75.7 3233 42.7 2102 61.9 161 10 4.8

5 21 62 73.5 2880 39.2 1868 64.8 166 11 4.6

6 36 73 116.2 4658 40.1 3022 64.9 140 11 4.6

7 32 67 84.1 3450 41.1 2284 66.2 153 12 4.5

8 27 69 68.3 3213 47.0 2010 62.6 155 11 5.1

9 19 66 66.1 2771 41.9 1896 65.2 159 13 4.1

10 24 69 78.4 4028 51.4 2597 64.5 160 11 6.2

142

Daily Steps

Participant

#

Steps Day of Trial

C1 C2 D1 D2

Low

1 5752 13302 1534 1006

2 6076 9650 3594 904

3 15844 13944 3365 3420

4 9911 9063 2812 2292

5 8891 10273 1305 3625

6 5257 5712 2476 3169

7 7805 8111 4610 3541

8 8554 10841 1599 2210

9 17886 19002 2833 3009

10 16013 10254 3316 2881

Limited

1 4420 11826 3296 3484

2 7906 7691 3921 5904

3 10112 12248 5380 5078

4 9225 5852 4801 4732

5 10112 11540 6343 5203

6 6661 8040 4470 5240

7 7155 7440 4366 5110

8 10185 11013 3937 4993

9 16641 18145 3038 5728

10 15662 11892 5265 4896

Normal

1 9003 7991 7853 8109

2 8114 10846 5844 5730

3 11550 14868 7578 8441

4 8841 8857 6572 7880

5 12208 11660 10571 9023

6 5855 6827 7550 7827

7 9194 7993 7191 8864

8 10777 9945 8122 9751

9 21016 15398 13856 8924

10 14003 12933 9175 10751

143

Plasma Triglyceride Concentrations

Participant

#

TG Postprandial Time (Hours)

Baseline H2 H3 H4 H6

Low

1 109.7 148.6 169.9 209.1 172.9

2 113.1 179.4 169.7 175.4 157.8

3 86.6 122.6 130.4 146.2 112.6

4 98.6 152.3 158.7 142.8 124.0

5 48.6 99.5 151.7 172.2 104.1

6 124.5 232.1 260.1 299.6 231.3

7 57.2 87.2 194.4 164.1 94.9

8 85.6 138.0 137.1 130.5 117.6

9 87.6 131.0 134.2 125.1 91.1

10 86.9 184.8 217.5 199.9 124.2

Limited

1 103.9 139.4 182.2 197.6 161.5

2 105.4 139.6 164.6 175.8 154.6

3 83.8 114.4 137.9 130.7 108.4

4 90.9 133.2 140.9 139.2 124.7

5 40.3 81.2 149.4 136.8 68.0

6 130.3 219.4 289.5 286.5 225.2

7 38.6 120.1 213.3 194.3 115.7

8 77.1 143.4 168.9 140.3 110.8

9 66.1 118.5 116.5 102.2 79.6

10 74.3 130.7 190.9 172.8 113.6

Normal

1 92.0 146.3 188.9 178.7 182.9

2 111.4 147.3 155.1 156.4 134.7

3 82.3 112.4 115.4 118.3 99.0

4 83.6 129.9 134.4 121.6 118.9

5 52.1 63.7 130.2 128.5 66.3

6 89.3 175.0 221.0 214.9 168.3

7 50.7 96.8 165.4 135.6 71.2

8 75.3 127.7 127.5 106.8 93.2

9 72.7 110.8 105.2 88.9 95.4

10 90.1 143.2 169.9 166.5 126.6

144

Plasma Glucose Concentrations

Participant

#

Postprandial Time (Hours)

Baseline H2 H3 H4 H6

Low

1 109.7 148.6 169.9 209.1 172.9

2 113.1 179.4 169.7 175.4 157.8

3 86.6 122.6 130.4 146.2 112.6

4 98.6 152.3 158.7 142.8 124.0

5 48.6 99.5 151.7 172.2 104.1

6 124.5 232.1 260.1 299.6 231.3

7 57.2 87.2 194.4 164.1 94.9

8 85.6 138.0 137.1 130.5 117.6

9 87.6 131.0 134.2 125.1 91.1

10 86.9 184.8 217.5 199.9 124.2

Limited

1 103.9 139.4 182.2 197.6 161.5

2 105.4 139.6 164.6 175.8 154.6

3 83.8 114.4 137.9 130.7 108.4

4 90.9 133.2 140.9 139.2 124.7

5 40.3 81.2 149.4 136.8 68.0

6 130.3 219.4 289.5 286.5 225.2

7 38.6 120.1 213.3 194.3 115.7

8 77.1 143.4 168.9 140.3 110.8

9 66.1 118.5 116.5 102.2 79.6

10 74.3 130.7 190.9 172.8 113.6

Normal

1 92.0 146.3 188.9 178.7 182.9

2 111.4 147.3 155.1 156.4 134.7

3 82.3 112.4 115.4 118.3 99.0

4 83.6 129.9 134.4 121.6 118.9

5 52.1 63.7 130.2 128.5 66.3

6 89.3 175.0 221.0 214.9 168.3

7 50.7 96.8 165.4 135.6 71.2

8 75.3 127.7 127.5 106.8 93.2

9 72.7 110.8 105.2 88.9 95.4

10 90.1 143.2 169.9 166.5 126.6

145

RER Data

Participant

#

Postprandial Time (Hours)

Baseline H2 H4 H6

Low

1 0.829 0.815 0.791 0.784

2 0.809 0.774 0.747 0.741

3 0.721 0.771 0.756 0.733

4 0.814 0.836 0.798 0.761

5 0.786 0.87 0.748 0.77

6 0.786 0.848 0.761 0.796

7 0.794 0.799 0.851 0.762

8 0.772 0.947 0.898 0.885

9 0.748 0.891 0.79 0.721

10 0.761 0.923 0.812 0.809

Limited

1 0.764 0.776 0.714 0.697

2 0.754 0.812 0.747 0.75

3 0.72 0.831 0.772 0.744

4 0.793 0.818 0.774 0.729

5 0.726 0.851 0.763 0.751

6 0.736 0.888 0.756 0.759

7 0.745 0.848 0.827 0.794

8 0.752 0.914 0.85 0.841

9 0.757 0.781 0.908 0.752

10 0.755 0.895 0.795 0.737

Normal

1 0.721 0.766 0.727 0.731

2 0.724 0.777 0.791 0.718

3 0.74 0.809 0.739 0.726

4 0.743 0.789 0.741 0.723

5 0.776 0.836 0.788 0.726

6 0.717 0.755 0.757 0.735

7 0.721 0.829 0.75 0.73

8 0.745 0.844 0.732 0.735

9 0.758 0.79 0.729 0.71

10 0.748 0.736 0.886 0.844

146

Postprandial Fat Oxidation (kcal/6h)

Participant

Trial 1 2 3 4 5 6 7 8 9 10

Baseline 417.8 512.5 522.6 417.8 512.5 522.6 417.8 512.5 522.6 417.8

Acute 519.1 476.0 472.1 519.1 476.0 472.1 519.1 476.0 472.1 519.1

Post

Training 301.7 231.0 265.5 301.7 231.0 265.5 301.7 231.0 265.5 301.7

147

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